{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Transfer Learning\n",
    "\n",
    "With certain data types it is possible to use the weights learned in one task to be **transferred** to another task. For example in a task that is used to detect Animals and Vehicles in images (as done in CIFAR10) could be reused to classify dogs and cats. \n",
    "\n",
    "Transfer Learning is heavily used in Image recognition and Natural Language Processing (NLP) related tasks.\n",
    "\n",
    "This tutorial is based on https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html.\n",
    "![alt-text](../images/doge.jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/JuRQAi5RR3A?rel=0&amp;controls=0&amp;showinfo=0\" frameborder=\"0\" allowfullscreen></iframe>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import HTML\n",
    "\n",
    "HTML('<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/JuRQAi5RR3A?rel=0&amp;controls=0&amp;showinfo=0\" frameborder=\"0\" allowfullscreen></iframe>')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: tqdm in /root/miniconda3/lib/python3.6/site-packages\n",
      "Fetching package metadata ...........\n",
      "Solving package specifications: .\n",
      "\n",
      "Package plan for installation in environment /root/miniconda3:\n",
      "\n",
      "The following packages will be UPDATED:\n",
      "\n",
      "    conda: 4.3.23-py36_0 --> 4.3.24-py36_0\n",
      "\n",
      "conda-4.3.24-p 100% |################################| Time: 0:00:00   2.84 MB/s\n"
     ]
    }
   ],
   "source": [
    "!pip install tqdm\n",
    "!conda install -y Pillow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using Theano backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from urllib.request import urlretrieve\n",
    "from os.path import isfile, isdir, getsize\n",
    "from os import mkdir, makedirs, remove, listdir\n",
    "from tqdm import tqdm\n",
    "\n",
    "import zipfile\n",
    "import tarfile\n",
    "import pickle\n",
    "\n",
    "from keras.models import Sequential, Model\n",
    "from keras import optimizers\n",
    "from keras.layers import Dense, Activation, Conv2D, MaxPool2D, Flatten, BatchNormalization, Dropout\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "import glob\n",
    "import shutil\n",
    "\n",
    "import pickle\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Download and extract the doge and cate pictures, as well as some numpy files which will be used later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "catdog_dataset_folder_path = 'catdog'\n",
    "\n",
    "class DLProgress(tqdm):\n",
    "    last_block = 0\n",
    "\n",
    "    def hook(self, block_num=1, block_size=1, total_size=None):\n",
    "        self.total = total_size\n",
    "        self.update((block_num - self.last_block) * block_size)\n",
    "        self.last_block = block_num\n",
    "\n",
    "if not isfile('catdog.zip'):\n",
    "    with DLProgress(unit='B', unit_scale=True, miniters=1, desc='Doge n Cate Dataset') as pbar:\n",
    "        urlretrieve(\n",
    "            'https://download.microsoft.com/download/3/E/1/3E1C3F21-ECDB-4869-8368-6DEBA77B919F/kagglecatsanddogs_3367a.zip',\n",
    "            'catdog.zip',\n",
    "            pbar.hook)\n",
    "\n",
    "if not isdir(catdog_dataset_folder_path):\n",
    "    mkdir(catdog_dataset_folder_path)\n",
    "    with zipfile.ZipFile('catdog.zip') as f:\n",
    "        f.extractall('./'+catdog_dataset_folder_path)\n",
    "        # Unfortunately some of the files are corrupt so we need to clean these out:\n",
    "        !apt-get install -y jhead > /dev/null 2>&1\n",
    "        !jhead -de catdog/PetImages/Cat/*.jpg > /dev/null 2>&1 \n",
    "        !jhead -de catdog/PetImages/Dog/*.jpg > /dev/null 2>&1\n",
    "\n",
    "# Download numpy files which will be used in transfer learning section\n",
    "if not isfile('bottleneck_features_train.npy'):\n",
    "    with DLProgress(unit='B', unit_scale=True, miniters=1, desc='Bottleneck features') as pbar:\n",
    "        urlretrieve(\n",
    "                'https://www.dropbox.com/s/a38gpvdcryw0kfc/bottleneck.zip?dl=1',\n",
    "                'bottleneck.zip',\n",
    "                pbar.hook)\n",
    "\n",
    "    with zipfile.ZipFile('bottleneck.zip') as f:\n",
    "        f.extractall('./')\n",
    "        \n",
    "    files = listdir('bottleneck 2/')\n",
    "    \n",
    "    for f in files:\n",
    "        shutil.move('bottleneck 2/'+f,'./')\n",
    "    shutil.rmtree('bottleneck 2/')\n",
    "    remove('bottleneck.zip')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "files = glob.glob(catdog_dataset_folder_path+'/PetImages/**/*.jpg')\n",
    "labels = np.array([0]*12500+[1]*12500)\n",
    "\n",
    "size = np.zeros(len(files))\n",
    "for i,f in enumerate(files):\n",
    "    size[i] = getsize(f)\n",
    "    \n",
    "idx = np.where(size==0)[0]\n",
    "for i in idx[::-1]:\n",
    "    del files[i]\n",
    "    labels = np.delete(labels, i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In keras we are required to place the training images in a certain folder, with the subfolders structured so that each subfolder contains the class. We will structure the validation folder in the same way:\n",
    "```\n",
    "data/\n",
    "    train/\n",
    "        dogs/\n",
    "            dog001.jpg\n",
    "            dog002.jpg\n",
    "            ...\n",
    "        cats/\n",
    "            cat001.jpg\n",
    "            cat002.jpg\n",
    "            ...\n",
    "    validation/\n",
    "        dogs/\n",
    "            dog001.jpg\n",
    "            dog002.jpg\n",
    "            ...\n",
    "        cats/\n",
    "            cat001.jpg\n",
    "            cat002.jpg\n",
    "            ...\n",
    "```            \n",
    "\n",
    "From the dataset we randomly choose 20000 images and moves them to training and the rest to testing folders. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 3, 4, 2, 0])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.permutation(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "len_data = len(files)\n",
    "train_examples = 20000\n",
    "test_examples = len_data - train_examples\n",
    "\n",
    "# randomly choose 20000 as training and testing cases\n",
    "permutation = np.random.permutation(len_data)\n",
    "train_set = [files[i] for i in permutation[:train_examples]]\n",
    "test_set = [files[i] for i in permutation[-test_examples:]]\n",
    "train_labels = labels[permutation[:train_examples]]\n",
    "test_labels = labels[permutation[-test_examples:]]\n",
    "\n",
    "train_folder = catdog_dataset_folder_path+'/train'\n",
    "test_folder = catdog_dataset_folder_path+'/test'\n",
    "\n",
    "if isdir(train_folder): #if directory already exists\n",
    "    shutil.rmtree(train_folder)    \n",
    "if isdir(test_folder): #if directory already exists\n",
    "    shutil.rmtree(test_folder)    \n",
    "makedirs(train_folder+'/cat/')\n",
    "makedirs(train_folder+'/dog/')\n",
    "makedirs(test_folder+'/cat/')\n",
    "makedirs(test_folder+'/dog/')\n",
    "\n",
    "for f,i in zip(train_set, train_labels):\n",
    "    if i==0:\n",
    "        shutil.copy2(f, train_folder+'/cat/')\n",
    "    else:\n",
    "        shutil.copy2(f, train_folder+'/dog/')\n",
    "        \n",
    "for f,i in zip(test_set, test_labels):\n",
    "    if i==0:\n",
    "        shutil.copy2(f, test_folder+'/cat/')\n",
    "    else:\n",
    "        shutil.copy2(f, test_folder+'/dog/')  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "View some sample images:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 20000 images belonging to 2 classes.\n"
     ]
    },
    {
     "data": {
      "image/png": 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t1fxAaP36qDnlBwNAkv3vRgskm1QSnyucNNFFdtMFgDUiBPvYEWWQnXynxuiD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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8cbc1cc668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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dJPn8IoDfNsb8DIBXAXx81A6dbhdnz78+dMzKoYPJ+2NH3XuqRu/7Wx/y286e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\n/yU3xfuD2dGqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8c860f74e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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CfZ12aVWNnp3cY+HGSNZ61LepxDuMB0cxQUpGxhgW61y8KC7i8roNjL5wR8zw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\nCKnMO6B52d2X4F7qtIaqs1J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8pseLBwtpMcBaq4mLM03l+6tOBdloIq5N4LENFSsbNTg1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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8c86054320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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9CsAHf1iT9MUXX37w8j0F/BzHmQFwD4BvAxhzXdeQj62BbsHrSrVWw7deeen6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M3zWjhmtQ1ENCiMvj/5CYuAmir5fJ76TnIN3PuYbuc9lXz32jvR3THkFPY44ogjryzTqvmrqivN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\n+fE74sgslf8P13yKKw1Itd4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8c860f7048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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38ScBANt2bOdtUGosoJ/Z7BOLIhrh3g4IdhzSOQNsknXLnJVo/DBrFJRsw4xp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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8c85ebb978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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nngUA/Nzd7wAAfOqPqPvtk89YCyf1QM3MF9gMxTxyp6S0JGzKFm5mhq2W4zXM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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8c85f67b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "datagen = ImageDataGenerator(\n",
    "        rescale=1./255,\n",
    "        rotation_range=5,\n",
    "        zoom_range=0.2,\n",
    "        horizontal_flip=True)\n",
    "\n",
    "img_height = img_width = 100\n",
    "channels = 3\n",
    "train_generator = datagen.flow_from_directory(\n",
    "    train_folder,\n",
    "    color_mode = \"rgb\",\n",
    "    target_size=(img_height, img_width),\n",
    "    batch_size=1,\n",
    "    class_mode=None)\n",
    "\n",
    "i = 0\n",
    "img_list = []\n",
    "for batch in train_generator: #.flow(x, batch_size=1)\n",
    "    img_list.append(batch)\n",
    "    i += 1\n",
    "    if i > 5:\n",
    "        break\n",
    "        \n",
    "\n",
    "for img in img_list:\n",
    "    plt.imshow(np.squeeze(img))\n",
    "    plt.show()        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic logistic multiclass classification:\n",
    "\n",
    "Always, ALWAYS compare to the most basic possible ML/ statistics algorithm. In this case logistic regression."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 20000 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "batch_size = 1000\n",
    "train_generator = datagen.flow_from_directory(\n",
    "    train_folder,\n",
    "    color_mode = \"rgb\",\n",
    "    target_size=(img_height, img_width),\n",
    "    batch_size=batch_size,\n",
    "    class_mode='binary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x_train, y_train = next(train_generator)\n",
    "x_test, y_test = next(train_generator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "logistic = LogisticRegression()\n",
    "logistic.fit(x_train.reshape(batch_size,-1), y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.,  1.,  1.,  0.,  0.,  1.,  0.,  0.,  0.,  0.], dtype=float32)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = logistic.predict(x_test.reshape(len(x_test), -1))\n",
    "y_pred[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Predicting the probabilities for the first 3 images:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.45270886,  0.54729114],\n",
       "       [ 0.44448103,  0.55551897],\n",
       "       [ 0.00495017,  0.99504983]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logistic.predict_proba(x_test[:3].reshape(3,-1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Accuracy of the predictions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.514"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.count_nonzero(y_pred == y_test)/len(y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Convolution Neural Networks (CNN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "\n",
    "model.add(Conv2D(8, kernel_size=(3,3), padding='same', input_shape = (img_width,img_height,channels)))\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPool2D(pool_size=(3, 3)))\n",
    "\n",
    "model.add(Conv2D(16, kernel_size=(3,3), padding='same'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPool2D(pool_size=(2, 2)))\n",
    "\n",
    "model.add(Conv2D(32, kernel_size=(3,3), padding='same'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPool2D(pool_size=(2, 2)))\n",
    "\n",
    "model.add(Conv2D(32, kernel_size=(3,3), padding='same'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPool2D(pool_size=(2, 2)))\n",
    "\n",
    "model.add(Flatten())\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "model.compile(optimizer='adadelta', loss='binary_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_5 (Conv2D)            (None, 100, 100, 8)       224       \n",
      "_________________________________________________________________\n",
      "activation_5 (Activation)    (None, 100, 100, 8)       0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_5 (MaxPooling2 (None, 33, 33, 8)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_6 (Conv2D)            (None, 33, 33, 16)        1168      \n",
      "_________________________________________________________________\n",
      "batch_normalization_4 (Batch (None, 33, 33, 16)        64        \n",
      "_________________________________________________________________\n",
      "activation_6 (Activation)    (None, 33, 33, 16)        0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_6 (MaxPooling2 (None, 16, 16, 16)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_7 (Conv2D)            (None, 16, 16, 32)        4640      \n",
      "_________________________________________________________________\n",
      "batch_normalization_5 (Batch (None, 16, 16, 32)        128       \n",
      "_________________________________________________________________\n",
      "activation_7 (Activation)    (None, 16, 16, 32)        0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_7 (MaxPooling2 (None, 8, 8, 32)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_8 (Conv2D)            (None, 8, 8, 32)          9248      \n",
      "_________________________________________________________________\n",
      "batch_normalization_6 (Batch (None, 8, 8, 32)          128       \n",
      "_________________________________________________________________\n",
      "activation_8 (Activation)    (None, 8, 8, 32)          0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_8 (MaxPooling2 (None, 4, 4, 32)          0         \n",
      "_________________________________________________________________\n",
      "flatten_2 (Flatten)          (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 513       \n",
      "=================================================================\n",
      "Total params: 16,113.0\n",
      "Trainable params: 15,953.0\n",
      "Non-trainable params: 160.0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 20000 images belonging to 2 classes.\n",
      "Epoch 1/2\n",
      "156/156 [==============================] - 387s - loss: 0.6443 - acc: 0.6396   \n",
      "Epoch 2/2\n",
      "156/156 [==============================] - 336s - loss: 0.5731 - acc: 0.6981   \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f82935d40b8>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch_size = 128\n",
    "train_generator = datagen.flow_from_directory(\n",
    "    train_folder,\n",
    "    color_mode = \"rgb\",\n",
    "    target_size=(img_height, img_width),\n",
    "    batch_size=batch_size,\n",
    "    class_mode='binary')\n",
    "model.fit_generator(train_generator, train_examples//batch_size, epochs=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 4998 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "batch_size = 1\n",
    "test_generator = datagen.flow_from_directory(\n",
    "    test_folder,\n",
    "    color_mode = \"rgb\",\n",
    "    target_size=(img_height, img_width),\n",
    "    batch_size=batch_size,\n",
    "    class_mode='binary', \n",
    "    shuffle=False)\n",
    "y_pred = model.predict_generator(test_generator, test_examples//batch_size, workers=4)\n",
    "# model.predict_classes(test_x)\n",
    "# np.count_nonzero(y_pred == test_y)/len(test_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correct predictions: 0.5070028011204482\n"
     ]
    }
   ],
   "source": [
    "correct = 0\n",
    "for i, f in enumerate(test_generator.filenames):\n",
    "    if f.startswith('cat') and y_pred[i]<0.5:\n",
    "        correct +=1\n",
    "    if f.startswith('dog') and y_pred[i]>=0.5:\n",
    "        correct +=1\n",
    "\n",
    "print('Correct predictions: '+str(correct/len(test_generator.filenames)))        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 4998 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "batch_size = 6\n",
    "test_generator = datagen.flow_from_directory(\n",
    "    test_folder,\n",
    "    color_mode = \"rgb\",\n",
    "    target_size=(img_height, img_width),\n",
    "    batch_size=batch_size,\n",
    "    class_mode='binary', \n",
    "    shuffle=True)\n",
    "x_test, y_test = next(test_generator)\n",
    "\n",
    "p = model.predict(x_test)\n",
    "p = np.hstack([y_pred, 1-y_pred])\n",
    "label_dict = {0: 'cat', 1: 'dog'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bReaCUqHCsDfB0Axm8xG5TJAEuF5OGE0ZDI/J8hDLFggBS0sN0iTHK5SYTidkWU6aSio1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RsEBRTKjqko21TdpJzFKvxdrKJcbjDV796teRZSlRFNFuxYThEq/5utcyHo9ZXjjILS/9WpSt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\nMzGxg2QySTqdxuPx4Pf78Xg87Nq1q5Nnizb1ep0jR44Q7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Wa8NDdMsx0I6cDniTPevxPL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\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RjUWol0tYhsljTz7J5ekLFA2Ltdw6mc0NPvboc4z3jiHZGmMDu9kzOMDU/j3IEZsLZ8+x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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f826a23a668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,12))\n",
    "\n",
    "for i in range(batch_size):\n",
    "    plt.subplot(batch_size,2,2*i+1)\n",
    "    plt.imshow(x_test[i])\n",
    "    plt.title(label_dict[y_test[i]])\n",
    "    \n",
    "    plt.subplot(batch_size,2,2*i+2)\n",
    "    plt.bar(range(2),p[i])\n",
    "    plt.xticks(range(2), ['cat', 'dog'])\n",
    "#     plt.show()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.28162625,  0.71837378],\n",
       "       [ 0.24552229,  0.75447774],\n",
       "       [ 0.15673721,  0.84326279],\n",
       "       ..., \n",
       "       [ 0.36343947,  0.63656056],\n",
       "       [ 0.29009002,  0.70990998],\n",
       "       [ 0.27991441,  0.72008562]], dtype=float32)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transfer Learning - Part 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras import applications\n",
    "\n",
    "datagen = ImageDataGenerator(rescale=1.0/255)\n",
    "model = applications.VGG16(include_top=False, input_shape=(img_width, img_height, channels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_2 (InputLayer)         (None, None, None, 3)     0         \n",
      "_________________________________________________________________\n",
      "block1_conv1 (Conv2D)        (None, None, None, 64)    1792      \n",
      "_________________________________________________________________\n",
      "block1_conv2 (Conv2D)        (None, None, None, 64)    36928     \n",
      "_________________________________________________________________\n",
      "block1_pool (MaxPooling2D)   (None, None, None, 64)    0         \n",
      "_________________________________________________________________\n",
      "block2_conv1 (Conv2D)        (None, None, None, 128)   73856     \n",
      "_________________________________________________________________\n",
      "block2_conv2 (Conv2D)        (None, None, None, 128)   147584    \n",
      "_________________________________________________________________\n",
      "block2_pool (MaxPooling2D)   (None, None, None, 128)   0         \n",
      "_________________________________________________________________\n",
      "block3_conv1 (Conv2D)        (None, None, None, 256)   295168    \n",
      "_________________________________________________________________\n",
      "block3_conv2 (Conv2D)        (None, None, None, 256)   590080    \n",
      "_________________________________________________________________\n",
      "block3_conv3 (Conv2D)        (None, None, None, 256)   590080    \n",
      "_________________________________________________________________\n",
      "block3_pool (MaxPooling2D)   (None, None, None, 256)   0         \n",
      "_________________________________________________________________\n",
      "block4_conv1 (Conv2D)        (None, None, None, 512)   1180160   \n",
      "_________________________________________________________________\n",
      "block4_conv2 (Conv2D)        (None, None, None, 512)   2359808   \n",
      "_________________________________________________________________\n",
      "block4_conv3 (Conv2D)        (None, None, None, 512)   2359808   \n",
      "_________________________________________________________________\n",
      "block4_pool (MaxPooling2D)   (None, None, None, 512)   0         \n",
      "_________________________________________________________________\n",
      "block5_conv1 (Conv2D)        (None, None, None, 512)   2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv2 (Conv2D)        (None, None, None, 512)   2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv3 (Conv2D)        (None, None, None, 512)   2359808   \n",
      "_________________________________________________________________\n",
      "block5_pool (MaxPooling2D)   (None, None, None, 512)   0         \n",
      "=================================================================\n",
      "Total params: 14,714,688.0\n",
      "Trainable params: 14,714,688.0\n",
      "Non-trainable params: 0.0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = applications.VGG16(include_top=False, weights='imagenet')\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Do not _uncomment_ and run the following two blocks unless absolutely necessary**. It takes almost one hour to run. It took me a while to understand why in the Keras blog they had saved the parameters. It isn't necassary for you to save it. However, if you do come back to the tutorial you probably dont want to run this section again. It is slow mainly because there's 14 Million parameters to go through for each example. Having a GPU in this instance would help tremendously.\n",
    "\n",
    "### Note 1\n",
    "It is however important to notice that I am **not** training in this block. I am predicting using a truncated VGG16 net. See how I set the `include_top=False` parameter above. VGG16 was originally trained on the CIFAR10 dataset so that it would predict 10 classes. Now that we are truncating it and only using all but the top few layers (lyer closes to the prediction), it outputs a (3,3,512) image in our case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with open('bottleneck_features_train.npy','rb') as f:\n",
    "    bottleneck_features_train = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(19872, 3, 3, 512)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bottleneck_features_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 20000 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "batch_size = 128\n",
    "generator = datagen.flow_from_directory(\n",
    "    train_folder,\n",
    "    target_size=(img_width, img_height),\n",
    "    batch_size=batch_size,\n",
    "    class_mode=None,\n",
    "    shuffle=False)\n",
    "# bottleneck_features_train = model.predict_generator(generator, train_examples//batch_size, verbose=1, workers=4)\n",
    "# pickle.dump(bottleneck_features_train, open('bottleneck_features_train.npy', 'wb'))\n",
    "# bottleneck_features_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 4998 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "batch_size = 128\n",
    "valid_generator = datagen.flow_from_directory(\n",
    "    test_folder,\n",
    "    target_size=(img_width, img_height),\n",
    "    batch_size=batch_size,\n",
    "    class_mode=None,\n",
    "    shuffle=False)\n",
    "# bottleneck_features_valid = model.predict_generator(generator, test_examples//batch_size, verbose=1, workers=4)\n",
    "# with open('bottleneck_features_valid.npy', 'wb') as f:\n",
    "#     pickle.dump(bottleneck_features_valid, f)\n",
    "# bottleneck_features_valid.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "flatten_1 (Flatten)          (None, 4608)              0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 256)               1179904   \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 257       \n",
      "=================================================================\n",
      "Total params: 1,180,161.0\n",
      "Trainable params: 1,180,161.0\n",
      "Non-trainable params: 0.0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "with open('bottleneck_features_train.npy','rb') as f:\n",
    "    bottleneck_features_train = pickle.load(f)\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Flatten(input_shape=bottleneck_features_train.shape[1:]))\n",
    "model.add(Dense(256, activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "model.compile(optimizer='rmsprop',\n",
    "              loss='binary_crossentropy', metrics=['accuracy'])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 20000 images belonging to 2 classes.\n",
      "Epoch 1/15\n",
      "19872/19872 [==============================] - 2s - loss: 0.5500 - acc: 0.7746     \n",
      "Epoch 2/15\n",
      "19872/19872 [==============================] - 3s - loss: 0.3681 - acc: 0.8343     \n",
      "Epoch 3/15\n",
      "19872/19872 [==============================] - 3s - loss: 0.3316 - acc: 0.8550     \n",
      "Epoch 4/15\n",
      "19872/19872 [==============================] - 4s - loss: 0.3137 - acc: 0.8622     - ETA: 0s - loss: 0.3139\n",
      "Epoch 5/15\n",
      "19872/19872 [==============================] - 11s - loss: 0.2970 - acc: 0.8736    \n",
      "Epoch 6/15\n",
      "19872/19872 [==============================] - 12s - loss: 0.2835 - acc: 0.8777    \n",
      "Epoch 7/15\n",
      "19872/19872 [==============================] - 13s - loss: 0.2751 - acc: 0.8850    \n",
      "Epoch 8/15\n",
      "19872/19872 [==============================] - 14s - loss: 0.2613 - acc: 0.8898    \n",
      "Epoch 9/15\n",
      "19872/19872 [==============================] - 13s - loss: 0.2483 - acc: 0.8949    \n",
      "Epoch 10/15\n",
      "19872/19872 [==============================] - 15s - loss: 0.2362 - acc: 0.9022    \n",
      "Epoch 11/15\n",
      "19872/19872 [==============================] - 13s - loss: 0.2295 - acc: 0.9056    \n",
      "Epoch 12/15\n",
      "19872/19872 [==============================] - 12s - loss: 0.2206 - acc: 0.9109    \n",
      "Epoch 13/15\n",
      "19872/19872 [==============================] - 12s - loss: 0.2098 - acc: 0.9158    \n",
      "Epoch 14/15\n",
      "19872/19872 [==============================] - 13s - loss: 0.2016 - acc: 0.9174    \n",
      "Epoch 15/15\n",
      "19872/19872 [==============================] - 12s - loss: 0.1953 - acc: 0.9202    \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f8c84709e10>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch_size = 128\n",
    "generator = datagen.flow_from_directory(\n",
    "    train_folder,\n",
    "    target_size=(img_width, img_height),\n",
    "    batch_size=batch_size,\n",
    "    class_mode=None,\n",
    "    shuffle=False)\n",
    "\n",
    "labels = np.array([0 if f.startswith('cat') else 1 for f in generator.filenames])[:len(bottleneck_features_train)]\n",
    "model.fit(bottleneck_features_train, labels, epochs=15, batch_size=batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4512/4870 [==========================>...] - ETA: 0s\n",
      "The accuracy is: 0.9258726899383983\n"
     ]
    }
   ],
   "source": [
    "with open('bottleneck_features_valid.npy','rb') as f:\n",
    "    bottleneck_features_valid = pickle.load(f)\n",
    "valid_labels = np.array([0 if f.startswith('cat') else 1 for f in valid_generator.filenames])[:len(bottleneck_features_valid)]\n",
    "y_valid_pred = model.predict_classes(bottleneck_features_valid)\n",
    "accuracy = np.count_nonzero(valid_labels == y_valid_pred.ravel())/len(valid_labels)\n",
    "\n",
    "print('\\nThe accuracy is: '+str(accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transfer Learning - Part 2\n",
    "\n",
    "We can refine the model further by adjusting the last convolutional layer.\n",
    "\n",
    "Note that vgg16 is of type `Model` and not `Sequential`. Hence we cannot `add` the top layer as suggested in the keras blog.\n",
    "\n",
    "### Note 2\n",
    "We are setting the trainable weights to be everything but the last convolutional layer and the fully connected (dense) layers. Take note of the number of trainable parameters in the summary below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "vgg16 = applications.VGG16(include_top=False, weights='imagenet', input_shape=(img_width, img_height, channels))\n",
    "combinedModel = Model(inputs= vgg16.input, outputs= model(vgg16.output))\n",
    "\n",
    "for layer in combinedModel.layers[:-3]:\n",
    "    layer.trainable = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_3 (InputLayer)         (None, 100, 100, 3)       0         \n",
      "_________________________________________________________________\n",
      "block1_conv1 (Conv2D)        (None, 100, 100, 64)      1792      \n",
      "_________________________________________________________________\n",
      "block1_conv2 (Conv2D)        (None, 100, 100, 64)      36928     \n",
      "_________________________________________________________________\n",
      "block1_pool (MaxPooling2D)   (None, 50, 50, 64)        0         \n",
      "_________________________________________________________________\n",
      "block2_conv1 (Conv2D)        (None, 50, 50, 128)       73856     \n",
      "_________________________________________________________________\n",
      "block2_conv2 (Conv2D)        (None, 50, 50, 128)       147584    \n",
      "_________________________________________________________________\n",
      "block2_pool (MaxPooling2D)   (None, 25, 25, 128)       0         \n",
      "_________________________________________________________________\n",
      "block3_conv1 (Conv2D)        (None, 25, 25, 256)       295168    \n",
      "_________________________________________________________________\n",
      "block3_conv2 (Conv2D)        (None, 25, 25, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_conv3 (Conv2D)        (None, 25, 25, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_pool (MaxPooling2D)   (None, 12, 12, 256)       0         \n",
      "_________________________________________________________________\n",
      "block4_conv1 (Conv2D)        (None, 12, 12, 512)       1180160   \n",
      "_________________________________________________________________\n",
      "block4_conv2 (Conv2D)        (None, 12, 12, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_conv3 (Conv2D)        (None, 12, 12, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_pool (MaxPooling2D)   (None, 6, 6, 512)         0         \n",
      "_________________________________________________________________\n",
      "block5_conv1 (Conv2D)        (None, 6, 6, 512)         2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv2 (Conv2D)        (None, 6, 6, 512)         2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv3 (Conv2D)        (None, 6, 6, 512)         2359808   \n",
      "_________________________________________________________________\n",
      "block5_pool (MaxPooling2D)   (None, 3, 3, 512)         0         \n",
      "_________________________________________________________________\n",
      "sequential_1 (Sequential)    (None, 1)                 1180161   \n",
      "=================================================================\n",
      "Total params: 15,894,849.0\n",
      "Trainable params: 3,539,969.0\n",
      "Non-trainable params: 12,354,880.0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "combinedModel.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can try to use the `adagrad` optmizer if you wish but you'll soon see that all the progress that was made in `model` will be undone. It will infact overwrite the weights in `model` and you would have to rerun the `model` training from the `bottleneck_features` section.\n",
    "\n",
    "Why? It's so that the updates are small and does not destablise the weights that were previously learnt."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.save_weights('fc_model.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "combinedModel.compile(loss='binary_crossentropy',\n",
    "              optimizer = optimizers.RMSprop(lr=1e-4, decay=0.9), # optimizers.SGD(lr=1e-4, momentum=0.9)\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 20000 images belonging to 2 classes.\n",
      "Found 4998 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "combinedModel.compile(loss='binary_crossentropy',\n",
    "              optimizer = optimizers.RMSprop(lr=1e-4, decay=0.9), # optimizers.SGD(lr=1e-4, momentum=0.9)\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "# prepare data augmentation configuration\n",
    "train_datagen = ImageDataGenerator(\n",
    "    rescale=1. / 255,\n",
    "    shear_range=0.2,\n",
    "    zoom_range=0.2,\n",
    "    horizontal_flip=True)\n",
    "\n",
    "test_datagen = ImageDataGenerator(rescale=1. / 255)\n",
    "\n",
    "train_generator = train_datagen.flow_from_directory(\n",
    "    train_folder,\n",
    "    target_size=(img_height, img_width),\n",
    "    batch_size=batch_size,\n",
    "    class_mode='binary')\n",
    "\n",
    "validation_generator = test_datagen.flow_from_directory(\n",
    "    test_folder,\n",
    "    target_size=(img_height, img_width),\n",
    "    batch_size=batch_size,\n",
    "    class_mode='binary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "156/156 [==============================] - 3369s - loss: 0.3049 - acc: 0.8695 - val_loss: 0.2076 - val_acc: 0.9149\n",
      "Epoch 2/5\n",
      "155/156 [============================>.] - ETA: 16s - loss: 0.2834 - acc: 0.8760"
     ]
    }
   ],
   "source": [
    "# fine-tune the model\n",
    "combinedModel.fit_generator(\n",
    "    train_generator,\n",
    "    steps_per_epoch=train_examples//batch_size,\n",
    "    epochs=5,\n",
    "    validation_data=validation_generator,\n",
    "    validation_steps=test_examples//batch_size) # len(valid_generator.filenames)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predictions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BXSIi831NB44vQcPj9DQdH30l1x0BR4GbEdamTYUuseric95HcQdmu3gNYKL0c3v2mxN8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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8c8427cc88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The probability that this is a doge is: 0.679866\n"
     ]
    }
   ],
   "source": [
    "from PIL import Image\n",
    "\n",
    "img = Image.open('doge.jpg')\n",
    "img = np.asarray(img.resize((img_height, img_width), Image.ANTIALIAS))/255\n",
    "plt.imshow(img)\n",
    "plt.show()\n",
    "p = combinedModel.predict(np.array([img]))\n",
    "print('The probability that this is a doge is: ' +str(p[0][0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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m+HfWiCU8LhUX7uQ9/lyUMC9oElitFoebOCMNOj/yJdVHkMMegJATTWaLu2wH13Dg74n7\nKOyZWD1Oq6nXqAPZbLZA6Lb9Or7Amj8v9NpN6aHhxdfXV922U6z1D4/0fpmM9Xu6XXh+3DNFoK6b\nRXpcWzKqAQUct21SC9WfUfYLXcKSUZ/TbuQhwmIpXN+4duKcP84qz19ZZefU3lfP/24PqfnPXIFp\n4GUcHda9wf+DcBE6JMvSVyDQhdI8ty6oiMedB6qk2misuF0Wl/SJH+HJPJ2ihBiCH2lLkeOk4cUY\n+sfqrTJEABZP8O199S47+2+7bX/Q1ONeXNPLcPWqlhWvrGhEUMP3xgEOkef63bEj0uD0qD4sjxbR\n1GosGZ7FElgeGnoLklXYPUjmvHqIOrsCHlBqSUk1cK8loqwZzIJYBdb+BVByDm247yWzpscodc3Z\nsAXIN9b44wnm9shTdY8zfW9pUbdJGD0hy5LWmxiG95y1lIQfyniBWovKoXjqfeTysmI5GTIB435v\n5jymLvr0x2cXn4x4hxMjwfUNcRpMBPEHzpOdz3OdoIhcluWT2fOBVZ6/ssrOqZ1tSa9YMda+a5FI\nkc/qz/MJyFx+VoT7lHOv7yIRNqeqlULrfwlrtcvX1OP3M+/Z0kTXlNO+jqnumLpsG6Wfr2xecvsc\n1vW9vR1FqKdTzU0XWIs3gk4ue32NPrb76s1v7t0QEZG1ZUV/r6KA6MKal5EiLbUBEcjJWI9P1ago\nQMuTVCOThVXlIQxRxjop1Gu98KJmPBY6/vgles/duqVZkOFQoxpCCZ2WXyuPBuqVGjUd78KGchzg\nqOVwV3PrtaDoaG1R92/X9da7fUNLqW/+QL9vDEENEZEEYpmjUuesDzHLbaD9D3Z12/7EX/cU88+6\nmhqpuzgWC3HSehghqcWUBUPEkib6yaDnI7vFtkYU1y4qxXv3tnI+JuAcsGinyIIy4LZ+9wQTM57o\nZ2ms79ejgKeATkPMCPB2j8ysn65kvCqrrLIf2qoff2WVnVM7Y/XeWfLISaqx+XzY71JXAEfm1Fye\n1hzhBDTQZqLHe/6agm3f/oEHjxp1DcsaNQ31JgNo+aEScKmpYbUNAK0UYWW7o8DSg7uqH9Dv63Gz\noLVUEyE0gaBeT0N4AmWDYwWTehd9qumFaxpuGoSKcV0BKItw/8IF3z5xc0vPaWNL3zvE8faPFJT8\n7C/9LRER6TQ98WhwrCHu+pZWLI5Gug9TZEsLy27bKaRoYvQzWFhfx1h04zG6DkdBCy4ev8ByZWVV\nlzZLq5pivfHWDbftcU+XQ+MDXR7de0vJVHceQpUZoXyr4Sv0nJYA1He6uA4rKzpuers86J8gcz0P\n0oRAnL57YcPrOh4e6Nwtgiy0uKj3wD0qQKHRZqirn8gseDcFWLiEZq7tjp//oujNbOvAXqct8Gh1\nH/9+vArWyVZ5/soqO6d25qm+GQ36E/q284n5KCg4q3DywxpppKXr76yv7aaCXp2mjywWFhXIKgr1\nLPV1fdpHNbRc7mskcHTk+wPERr3R+qaSeRKkkfZ2VRNgGBCCMoBrDTTvHIOa2gSoNJ3otve3B26f\n5RXoAkALcQvA0/K6ft9rr33Mbbu6rt50aVm96wFSYkMQU5ZWrmBMHtBqLamXe/EVRCXwUnxdWfKp\nUKdRX1JNSPA9erwFbEsKrIjIcKjffXdbI6LpUCOAHOnCSy+84raNthXQ+8r3/pmIiLzzQDUY0rrO\njyO3BBoJdQB5pNu2EKXxuiYAAI310QI9PFOXjAqmIOr0jwPVH0RJQ0Q1LBR6EOv1neKaNlNf2DMC\nPXg0mm0Dv7Kq+4bBrCuOwv9ZvJSmT6/B8CSrPH9llZ1TO2P13tm1UKjRP18O+jgrTlAtfSrzbCGM\nAYQLpM5efP6K2/TOfTz5R+r11jfVK9YaOu61NtJGwZp5NFIvzd5tm6CVrq2r/t9o6L04+8o9gEer\n47gjKMF+6Gc+KiIi3bY//mWUD/O10dTo5NpzL4mIyGqw5mcL8yOkFMdTeBF4zhFaRQ+HPlXW6eg5\n1WptfKbbJDWNjKKGTwuO+1i/5+rZMhy/zPT/A3j8MO2VIlU5wbb3H+q5f/Mb39Axjf2297C2v4dt\nyjnF4g7KZrPJ0O3DVGXTKTPBYwKX6XT13DttXwyUoQjHUaCAUZSZzsH2w223rYFeZL9Hwpcel8U/\nDcxtHtDUJuweBIxiig5T9OZJUMuUUwMS93kSfiie5BY6+5Dk8zQxQOX5K6vsnNr7Qu89ieTzwwpK\n/LDmQH/2ywNtNo3bbht6lG5HvXYB2ufdbfVEGyuKwq9t+mIgPpJ7QNbp9bjOa3c9Wr6wpB6Aa3Ku\nRyPwkq9ff1FERF599brbZ39PvdDVq+rhm031NFsXFJ2fBKWklv0AMO7hUNfQrSYwBlBTFxd8SWkM\nwYmDPXSvGWrUk3Z1G5P79fVocIxzwxp5DFEMaOOPoDS8H3S5rSHaW0DZ7HRZqcwvv6yRy2Tkvfgr\nOP+v/CvtqnQMld4xevMNEUXVU38bNxsJzgn4DFzhIc5nf1uJR/WAbLWypGOIWdKLqGB1GfTtoFiq\nB0IWo6kddPJlbz4WmU0CXcEUWoCMAFIQnNbW9XtLG1wzZClKS+yL/vnJmvwaAVQlvZVVVtkT7Keu\npPc9G+mSMYuAuL4DIh37p3Aba8o6vIg09f+XkQWow62MJr7X2hQU2hRrtTrWgGOg3CxOERFZXtHj\nXrmiXpwcgVc/+AEREemhSCjsxvPCdY0C2P/9mWtXMRZ9jvcH3nOaSL3IMTzvCJkGg7LUdXi2RkB1\nHQN9F1KY4cEmAx2/6XiUPIGwyuFA1+ZjaOLX2nrcRUQLvX3vtRrgVTRQXnz9pRdExPMIZOrH70Qv\nog+JiMgw142++o03RETkwgWNuJYRPYiI7O6omMo9dOtN0bmHc5sg8siCCGN0qH83W6AGR3o+g0P9\n/9ZFL9O2v6/XhMNlx54G7xVc78nUe/7YKRKj6++aRnqLS4pL5LnvFcFtyENhUiqaE3g5yarCnsoq\nq+xU9kTPb4x5RkT+BxHZFH2ufMFa+4fGmBUR+ccick1EborIb1lrD97tWGT4zeu6z//9mHHo67xw\nxMkbP/4jlvYWKENl3S+8WD71XvzyJV1Hp21d2w/BvNs7VM9wCE8XB2vCEuKhh0B/KQm1AnygGRTR\nLCCi2LqoHuz2LS0r7iMKeeFVzXlPRz5D0Eb0kcBzTjGmzGbhaehY4N16wChG7CazomMoO4o/jIJe\ncdMxIxSdn05XPb3N9FjH+7t+LBC8HMAj7+yhkAflrTXMbSv162vm0jfXlANA7OXSRfXeplx02/Yx\nv6sr+toA/vBvf+Yj+n/gHa+//rrbJz+6KyIiF9Z0btnRtwd0fliwaMff+uR89IHxsBio36Mqid/2\nIaTE9g4U70hQIsxskcU5kwsiIlKCNxBhbb++rveEQSFaUYTYF0uOMTEUpXWirif3bRCBEMiPWMAz\nF5H/zFr7ioh8UkT+Q2PMKyLy+yLy59baF0Xkz/H/yiqr7KfEnvjjt9bet9b+K/zdE5HviMglEfl1\nEfkiNvuiiPzGj2uQlVVW2Y/engrwM8ZcE5HXROTLIrJprWU/6weiy4In2pPC+/nP51OA5qloDDNH\nxvH0lFkgxPZZJVsfB6SKCwB6HuxoOPj2D7TohOBLDbp5taDfEv9+5rLSbhlqd6EAc/W5F9y2LbSC\nboN62mF6CmmdDpYIeeLDZsq9OYVfpIIGA6S9Ao3DIUhEg76OH2XksvGMhtajCVRiAvptG4UkpOFu\ngNK8e6gruv2Br7ffQCv0EkslttGyIP347JS/zUivpmKQQeH98oLORS3xhKYc/amfuaSp1inITyym\neeONvxERkUvrfqlwaUPBwf5Q971xU4G/hUWm26j86+eJjHOuKEnJjksQkdDaTUQkQ6oz5n2Cdctk\noHPAVGuj5UlEEbT4u10dw+KCvmbQDjRho9eS15UvWNYxZXySenLw+jRU31MDfsaYjoj8LyLyn1hr\nj8PP7LskGI0xv2uMed0Y8/pg9C5iG5VVVtmZ2qk8vzEmEf3h/yNr7f+Ktx8aY7astfeNMVsisn3S\nvtbaL4jIF0RELm903rv8yHskA7mSSOrYwT35UknvEUYow93fgQY8CnhYPOJIGy1PDFrdUo9/8co1\nERG5dO15ERGBtJwsrvjyUHJTFuhEcGoFAKIS5c2hxluzAVUYAHOkEddBmx4HhUPMNuVoV019enqH\nfXSxsVOvNDtBc9O9XQ3qbKYAVO9wB9/no4TjI/VcLPsdku47RSFUovt22t4zO5VhulkAgAmiGzbj\nFBHZXFdQ0HX+Ab337j0F9ZqgVb/6qi8G4nFu3tZt2EHp7l1VVnr4YBfn54uZ8gm1BjGkKfs/ACAN\nwEFW4ZDsNILyDnX6SF9Pg2qdDJ2e1jcU2O2CVDWBJmQ0o0s59xOZ0+k/qbDnRPTvFPZEz2/02/5I\nRL5jrf2vgo/+VEQ+j78/LyJ/8kONoLLKKntf7DSe/+dF5N8Xkb8xxnwd7/1DEfkDEfljY8zviMg7\nIvJbP4oBPbLGf8L/T3OM0CzWVFOszcpIX6m9xu4qIiK3b6nS7r27+oTOQeZhm/Clpnr89a0Lbp/r\nr2kaqkx0zdeFp1/dUPxgMPRedhGEmQY09dj3bYoUncFascj8mHjBnOAH1usROvn0jr3nn8ArjaYo\nVYUuPXsXJClSXMc+Q3t0rB6+jzLf0bHOS2J0LKtLfi17fKhpr2EfFNdcz6OPc2xBZdcE+iwp1tOk\nMlMJuQYBjbAjE4lMOc6fhT0rC4qFLHfbeN97zglSrNeuaInzIVJ8+7tK/sHXysa6L03e29XIbgSx\nlgloyhPQlruLXrcwz9gfkC251cOvLWqqkthLWMAFTRhH5zWGQh0kmvlzpmYl1aYZ6ca1WT990to+\niqKnosk/8cdvrf2SPF5l+3On/qbKKqvsJ8p+Yui9J65lTvj/aWy+H9ysgYwB1N8/YKmT5PcdDdUj\njOEFWbjSRiHIM1eeFRGRax941e2zsq7EoJXLSrtNUXJLYtNCINlEeiyRb+t08PX/GbxMWBI7LtVT\n0hsOeuq1U3iRPCgo6Q90m6MeiEsYQ7era/DdfT2vcd9HCyWwhAk8WAIiz3CiHrRb935g96HiAgNE\nDgUKbhZAHqrBo7GASERkZPXvdptkFnhBdFacBpkH59G53mVxFIp3WCw1DSS5EmAiMei8XIu/+IJm\nWTbWNEq7Cfqvjlvnt90EiQtFQAgaZO/Qzw97EJD63UTvRRbgNCEpNux7THxlS+eju4ByXxSREV+a\n9eqzsnWUZTZzPSpP8vCVjFdllVV2KvuJ8fynNfaoE/HA/w8VLVDkgSxKPFmj4Ik67DNPrk/xPVBb\nJ2PQTbu6rty67pHdRXj+ZlfXlKT3FkDEZeq9IFm1O8d6vAIFN3QEe0DjwzJXFsuMsaYc9tXr1jD+\ng6B7TYxeghN45BwiFTduqBBmDo+3EJTEPgAKfgAKc5drZ3jD4aGXLDPAKu4CWe+0dWxdSKJNRoxS\nxBsuzfGBHodSVoO+/n8a4BspOBMUtEiB5BsXEOi1qieBBj/+xlRKZNCrD8IrQ0RDYc/EC1uKx5AD\n8MY3tYR4Z6TzM576+ymGvxxC4IViMCyaouhrq+VlvDoQEaV2DXsIsB9f2NHIlfDC4893WXo3e9qy\n+MrzV1bZObX3xfPbuSIFkSCX+wSGn4hf05Yl87FEiskam2vPo1vjHfRjg1iCQHAzirKZY4mIrCzp\nE/vGDfVsa5CZPoL3yBE9TEv/5I4S9mCHJBeihwhIeB7k4XsoJJkgz8zOMIlbv+uYeseeVSeFeqd9\nlK4eHmh00AXyPQ0iI2FGA+e8D9np1l1de5JhODr23jbD+rkPtt7hXoLvA42j8NsSvxihjNmUEBwt\n7uD4ipJTUktEZMriInjMvf09vK/z06j7kmEKnJIsN9+h2JW5BvcIxTjZp3CxiyhkouN+/jnFaUKJ\neB5vZ0/Hsr2t1/vWIbIhez5aixN2GUafQHQNakKgg168EciJ1yEiSqEUjpuRzWDgQ6OyZIEW8KCg\nm49+7wnmWg6Zd2+AOWeV56+ssnNq1Y+/ssrOqZ152B+G1SE543E2T2sMaiBcqqdW0/AzqWmoxdBV\nTHh86N3Pg7WXAAAgAElEQVSzgAdAEAE+Py4fDm5tarzZbumX7h7ocVsdrc0/7GvIvbbmSSBpjIaN\nIwWw+ujCM0YY3QrAKdJh+0PQY7EkoDbeItJho6CY5v4739c/EB4eIU03BiGFNe46Fv2ug0Md581b\nCvQlCFGff1lVgfa3H7p9NlEAszDU+bl7W79v557uywahIiLNVEGzmNRiAGbH0O2P6zoHy5ue0mwR\nutdB9hntKijYhu5fEWgLJAlVeLAPbtcagD+CbXHNx7q1GuY3Y+ccnac6gLgsI1rov2cM8hZblg9H\nWPoAVC2NX2rmAOIIDvIOayGEz0HVndHwQ7o0wrxPqBtA0DkJ9CDAyTZIgUYxlgxctp4Q1luXJn+3\nFPejVnn+yio7p3bmuv2PU+yd9/CPa+Md/pc94Qj0EUhx6RIT7geghlweFKhYAH1lQcLFo70AP/DB\nl0VE5P/5i78WEZFipNP2ysWfFRGRWulhmAHUbChyOxqpZz6A6i4psCIipSPv6EBZrCNQA7r9UIGz\nJPBst97RVtYWYOACSoVrGHeYCr13T9tH70F3jvrzm+gmxH26ASCXj0Fs6us493Z0X6b6Lmz4voEP\n7ykVOIaHnkKLv9cnuKnnk0X+Nmt3NB04GqrnzECcWl9RMLUTlMKKzALDoxFVhgAaIuKIAyWcPCcI\nqNc7gee0BGUBpmYB55hEncP9myIicoBrSNXe0ENSrzHCObGDz5iqyShnfmbLV7g3F/XcJujIVMa6\nT0nAsuajwZStuTOAvriRGFGS61MGIQD/Lks5jcivs8rzV1bZObWfGJIPPTy7+JymoMeWfHYxAuC+\nj6YNPVmixB5I7VliAfi08PuMQDhZQc+7v/XLvygiIofH6jVevf5BERFJgvxLb0dLR8cTkHDQ0y1H\nKssGmMIURSi9I5J8dExrK0oQQq2L/ODN77l9KJjxEF792Zc0KllE64DjI48P9HrAEEYUmkCXYZBM\nOm31+Ds7vhq7D5LPMYhHu9u6bp9wHdzzhUkNeL02jncAFdy7DzVlFqUoJW4GkQXSjm1gE92uUqX3\n9vR7Ht5/4LYlBZhahyTOOEqwW/N7H8b3WNrr0sooZirhSU0QIfWBUSyBQPXhn1G69tHrOu+9gU9v\ndtGbbwKCztD19dM57qALz9qmLxxabAOLQtRHKrYr1Q7whzgiMUuPPwDB65j3NiIObiciUq/ruGv1\nptjoB3Jaqzx/ZZWdU3tfPD8JPSHyH891330cRdcET0k+Ma0laUI9wzSj9NE02HS2Nx+f/A4pda8h\n2qzeKQOZqIFOLh+8qv386JH6AQnnYF+9KD09sxPsLjwtAkIHvNEEiP0IPetZOvzwoXrB/V2vmHuE\n/vZc0xJVPsC6/rAXiCwBmd7ZUU+8gIKeK1efnXl/MPKe7Ro+e/1rX8b36dg6WBf3g20t1tw5ZMJi\n9PEbZDrekj3wHvqS4Y1NIPbwXANkIo5BsFlZDoU/9Jq1gAP4tf945vNm03tBQUGPmc7StSP2s+M9\nkfv7q4HzmCJaKFBcxMKk9VXfZWkPUcAQZdLs0Uek6EM/+zMiInL12kW3Ty1H9gOFPFTrhRaM2MhT\ngceZjmX9gt5jF56FZNlDvQ51CMesrXl6MrsGTaO6lPFfyWmt8vyVVXZO7Yw9vxFjzLv26ntccYIv\n+fXvcY1P5JVdUInsxwHK7IsqgKbmRE9ZQKHblsaj/ZFRj9Pro6ceevK1lyBqeaReaxCIYbBzrcxl\nD1IIf9iaL+mlcIWpqee0GPch1urf/NZ3RMSLaYr4PD6jpzV4oB407tn5V0SkjTU9MwALC+rBFhd0\nnf3dtzSH34z9nF/+uHYGbmCbW7d0DZk09HuTgH7bB/22t6/r9TKCnFmir70j9eajbV8MREilj3Ea\nXIcYnI2Q+5HjsyG6CJE66/L/KEiKgvETBM+wbyQsiSWKjl54YUocc5kh28JuPoMjva79zN9HQ0QU\nx8jHl+5+RWSJyKOwPsKb8p4Cyj/Ad5umevVh6T2/bStW0FxRLOfYoIvQml5fCz7Bdx74e26a6bVp\nNFsOUzmNVZ6/ssrOqVU//soqO6d25oDfew37o4DfS9Wa4QDtkFABVYtJo/Qh5Bja8qyWMtNZ3XuL\nFGARhpAJdOuWlbDRWUTbLlSxbe9qWLu/fd/tAyaqpKSxNjQNU7DtUhCVsbVXA1r5OdKRw7F+b2dJ\nySF3H77p9mkALGoCBKNS0BANOlsBSeb+/fs4Z93nE5/4pP4fc3mEFNcgUInZhkLxcx9Q6u/Dh9pC\nrNGk8lFQVUkADmHzQ3zfZIqmoUhzlgGhpgHgsF0HOQatrhcw7vHYk6COj6HUMyWpR4+bov0X22yH\n7bYT/N0BeFd3+vqo88cSyNF89aR0HwC6Fy6o2s/mGvQMH3hAdwpylYAeniPtmICos7Siy6XVVd88\ndAA4sACAvLuj897oaIPWo9y3SB/Feo/1jAKvB339vk3ShnGvF2XQCwEtwjYuXHApztNY5fkrq+yc\n2tnSe8VKbAphkY0NtPZIdTQckpkj+1B112eaxABIqUcARSb6lKcnnR55BZym1afrFBTRVkuf3FPs\nW+uol40WgyIUgHRNpJ9ICS0O9cmdDgAeTf0z9B4UbVfRlWUKem+UgqwRNKKMEaEMtxVU64BmOwGR\nJhrq8TcW/D5TNHvsdvUc9/aU7DPCPmknaFc9VK/xoY98TERELr2o3vwbX/uKiIjkE00Lmsaa2+fh\nQOfpE7/4d0VE5Otf1644R3tKNW4GgVkJ8Guyr+d/2NOLcwAyi0XktdT2Y4qsnksx0flPE/0/Y49J\nFtBW0XUnh2JQrab/ryE4cPX+dR/txDGjATQJBcEmQaSRQt0oCrT42c+A1NqtS+p9P/lxJXEtvPm2\n27b19m0REdnt63zvYIxlqudh1hQw/WbPRztvPdT75RmmWEcaURx9S+e/0fHnvLCC9u8pKMYgJe1C\nVbmOqGfvwLf1PthT8C+S2KlSn8Yqz19ZZefUzn7NL0ZIx52h8TBHA8pu6QoYsDaD509L/5RnYUoT\nbau5HI3x2grSalSjrcdIBwp14fRJ2+iq9+tuXHb7xPC4I6jX3L2jHvrBbX36H97Wp/FuQHxpQ+O9\njqjhzk3dZ3VDx/Izn/6E2/bb3/muiIj81Ze/KiIiEaKQrU3FGBahBd8MacpQ0U2RehuCZNKBKs9u\nzysFrW4o0eQjH9e1/qRgbwKdy5UVTf0trF1x+yxtXtI/6uq5mkuaevrql/+FiIg8g3SniEiG9mv7\n0L0fI3rL6VMQvYXp0/tI+42Qvttc0zFQK38w9dHg6orOPwlBGa5DiXNm3neS+VRoMdXjlhalwvD8\nq2vqzZeWEBnEfs0fsa028ZqG/vHcVZ0/W/q0XYSo9e07uzhFnafbhzoHr/+VkqO2nrvu9tndhfLx\nop7jyqJiCkcHOsbd+76keg04TxP0c4NCoWYDHYKG6vEf3vi222d/V+fUDA5kMvTR7pOs8vyVVXZO\n7Uw9vxUjpZMlEJmpuUW5I0sy4wjrdxB3arE+wbsS0D/x5I+crp8+LQegy1LAQURkQuosnGhm9bOL\nF9TTr115SYfR8MiroHClHOuTmwSRu3fU8+/dUq23PPW9+i5eVQQ3Ayp/CM386x9RHf+FDd/d55v/\nm3Y424PXSFug476tCPvf/c3fFBGRv/Pzn3L7/Nn/ofs8eKDbJGCrRCCVbFzwtNJ0Qb3diy+pFxpA\nJOTiZR1jJ0GPgUvPu31WLuo++/d1nRsBhyjreo639j19eDpGhxuQbwpEb3lBHUOIfASMmgLX7HBH\nI7H70PBbh2duN3201sNaeXkZhSsRi3PUG3aRIZgMvOc/PNB9qPG/hO4+SapRFJMheaBFmNb1PJro\nlhzje5pNvUdefu6q23YZlO6FBRU3ib+v90JpdAyjHZCiLnicYx3ErAVUarWQtdh/oB6/CH4HCf7e\nvq3XNwcdeaWh9++d2/q9SwH40lpH4VTec119T2OV56+ssnNqZyzmEUmUNMTAS5VBcUWJvGUMNd1W\nqk+zFvLYdazf06mnl3J9ZyBhxS42BfLwQZpfDHKsVMp95qrSJ9cvPyciIrWWPqnL1HueErnb3rEi\n6ndu6dOYGu0bKNucxAEOgbwyS2tHoOY2kH/+9re/7rbdgVhHis8yrJn7iBoGiHY++6u/6c8ZY/qf\nvvjfiojI4baObZzqnPZLT6V9fkPX8hEQ7o0Lup6fIgq6uqnr7dayF+jo53puffQqeOvGTR3rkb6/\nsuQ92hG6+46R+2Zn2hhdgTOnZOvnNMbfAxQvHQ/1mj04VA/abfu53AAeUGBOP/JhjWBuva28h699\n/VsiIrLY9pFXgWKpo30dbxorEk6s4QKu2XPP+sKYRUiXWUSD7TayR3CNoXTZOtD41z6oHYDIt+jc\n0Ovw8Ahdlo7uun2ysa7jt++o137+5Q/ge6C4PPWYwjY6EFPG66OvfUhERFpdndMORE/Czs276O+w\nt7PrZL9OY5Xnr6yyc2pnvObXdaGBR7N5wMAb6d8dlGd2W+qNmhAqyCfqRaaTQGMeXW1rQGNH7GwD\nNtfuoUc+FzZ1bf/axz4sIiJ1iHCy2KLeUY/Awgn9TD1wDX39yBZbBqLfoBjn1NP2XMcArNVyiHN+\n48t/KSIidwc+/5v3tfzXYPxjCDuuXtJo5LVPKkrfDzzDd998S0RE1pZ0/BnKiXssShn7PO8BynH7\nQMdfePlFHf8S1vEj9e5lsP49eqieZ/fOTT01lAgbRGkHe0Ffe/A0mD0wWNvX4X06WDNfvLz5yD6H\nKIbKyeNA1DAMhC+3IR3WXVQP2UXm4SV4zje+/g0REVkNopHVZd2mh14EYxzv7gOd6wMwGCcT30X3\nmcvK7bh8cQPnAY5JC0VZNf8zcXr8qBS7/qLiAW2US28foZNP3Ucj+b7O9xB4B/v8/dwnNfNz+46P\nEj54XbGnJjgNffBG7iOj0hvo/cQoVMT3N8wmY5nOtEd6d6s8f2WVnVOrfvyVVXZO7czD/swaKVFc\nkcQ+xO4sQtOthXrmuoZKpkC6aKDh1HjoaY0RWld1oRVXxyuXEGnbK7BceUmVdtevKmjERoumpiFY\niaVIqBvPGqImUjWb6xoephFVajUUqwfnUaANVAcgXhvh2+47Wpt/6bkX/TkbDRXvTjQEThI951/6\nNaXWvvpxDQv/4l96dZa//NKXRETko1CKuQVyzBjkGOrUi4gsr+pcAgOTFK2t200Nw+/cAOV497bb\nZ++mgmk3vvWGztMuaKVgXWUB4aXA0obFNE0smRoI5VfRhHNj1fc1uL+jxxNQpSMoExdsvVYL2nUB\nMByDRPXmd5UUNepBmQjtwha7HiTce6DFRUsorImQKh7jWlncM3tBCy4u5xbRWpzaidbq3NYb/vom\nWAJQhYrFQFevQGGHTT2D89i/DVJbW8G6rau6BN0BHToLQLpvfkfvk3yg98S4p8uUItPxd9H41Yx8\nsREbrdp6Lg/nWnm/m1Wev7LKzqmdcWGPkdTUZATv2Gh5oObi1jUREakjbZahdLF/DFIJaL554lVP\nmNYYQz43wzZTvH/xmWfdthtb6il7IN2sLim4M6D6KhtItvwTO6pDuw8tlmtQsSEDdQwRtunEg2xU\ng2mDyLEOOuyDBwrq3Pzed9y2y6v62bPP6zgNVG4Ht9X7/u9/9IciInL/zj23z7Pr6p12HipBJIWm\n2zLSVc9d/5Db9td+87dFROTac5qWIixZIOphxFSm3lu0UMY87Gn6iMK41Lav5R6Qy1AmzYYzG/DE\ndaSptuB9r2x5SjDJNbc0yylTzL9BoU2ee8JODL52gWjjEAVVx9DVp2JTnnkQ1SBdSiIQtf0Xu7hv\nStC7gwiGTTx3oJWYAsjtgPpdBsUypVMOpjij6/EuIiJNNCdNm56M9oG2phX3xzrPX/+qUoBv3tEo\nJSv88Q939VovNKFMjDT2GF2b7Jj09NB0LK1mw6lDncYqz19ZZefUTu35jT5mXxeRu9baXzXGrIjI\nPxaRayJyU0R+y1p78PgjaLcRMxWJrT6F2wuX3Ge2oZ45QUltP9In3dEIVFE8jfNmkEIZ6brtCDjA\nGP3xrmBNdfmaL66oCUQkmvpknvT0iWpZpmugFxeo36YWZA94yFpDv7uAaMgEabVh368fKaZQA05g\nQBe+/PwrIiIyDQov9uDJLCqR1kArbY91XWzf0df0yBfrvPjqayIicvuBPvt7D3WbD39MS0k/8fP/\nhtv22nOaEltFVFCMEN0gRbpk1HNn1qe96CCXUJJ89Zpeoxypvrv3Ql19nQ/q1q2gG48FjXgMpeHF\ngLjz4Q9qmWwJ7cSvfkPX8UcQ9QiaE8kFRAwrSE0+gFiIRWqrU6fGYhAtgKRVp19DBBBBvbkBCm8j\nEABpISUpuAcG6FrUyHUy0mDbOKbKM8q54YNj4B1RrPvsH/g5vX+k4z5ANHsTRK8RrkMcFBktxOjj\nh3OcjPXaE5tin8XQJoik8/HY0adPY0/j+X9PRL4T/P/3ReTPrbUvisif4/+VVVbZT4mdyvMbYy6L\nyN8Rkf9SRP5TvP3rIvJZ/P1FEfmnIvIP3u041iSSJ5uyelE9s+l6z//OaLYTyb0jfZL2ciWz5BBu\nyAP5pYfvqBe6/6aWN/7cS7q2/dhVRfYXlj2F047xxIYSa1aot+6DWDNFWac0/fOQCrnZlGv72Veq\nyg6HXnoqjtG9FRmNJjwRUdragu9es3FRx0f5LqLnLG89hjc8PPbRwutf/6aeBwqfOouK6F99XqOc\nZy778twa1n89EEQGY42MoqHOW3YELKHwAdvWgnrTj31QySYGAMfNt29iTsJeCOwug94BY0QuqUYA\nPXT9+dK//LLb5/mXlMD08rM6ziXQY++4iCLouIv19PEBkO1cv3vBVeeASBWIwlDDn9JlCTw1pb7Y\nsy9NPbazuKTr8wQABzv9jkGDrteDDBBKqVnEREpzAxFehp/UwZGPII8OFZ/5wTuK+5Slevy8ZDdp\nTxLLiB/F7EWBQjNgFxl7IQT03gT4Q1mWs/LWT7DTev7/WkT+c5ltA7hpraV43QMR2XxkLxExxvyu\nMeZ1Y8zrg0CCurLKKnt/7Yme3xjzqyKyba39mjHmsydtY621xpwMM1prvyAiXxARuXL1eVt78Vdk\nB0DrWzd33HZcR+8d3xQRkSme6otLutahWMX2je+7fY521Hu/el0pu7/wKc2LLy+Dphl0P41b+iQd\n4wFkDHXiqb+u/28EhT0UwySCvLqmx+2haGc6gF59KEeGUlJGDRSkpBxZNyhCifDEHmNdOoYIJ3ED\nfj+14kVEduAFC4hCfuwzvyQiIp/7N39F90kCzsEA8lEoZtk9UA9fHKknapf6fZ1WIAYJKSt7QSOu\n9KPat24XYqUULRUROUD3Gov16B7W3rsofFpeVa8VPvS//22N0hrInVtmWRDZlQFn4hj9Adnbroty\nWkZI1HmJJIzW8F5ECTBcXwiPpAmFYP11ruGcKe3GNX4N1z0LsjkTZJIaLd2fsmEUnaHU2MKiP34H\n2Qlb7OC4+j4zDlnuo51ovucEy6HBs+CWtVDoFp/Vk8R1KDqNnSbs/3kR+TVjzN8WkYaILBhj/kcR\neWiM2bLW3jfGbInI9rsepbLKKvuJsieG/dba/8Jae9lae01EfltE/l9r7b8nIn8qIp/HZp8XkT/5\nsY2yssoq+5HbeyH5/IGI/LEx5ndE5B0R+a0n7TDII/nybktqpD6uePptB9VSay0NjRahIXe8pzXQ\n0aEGFmsHfqlwBXXXf/tTqnSzuaakkhSKPtnUh5s5dfmRTisGAO/QUJM44mLDt1ZuQcWmQFi1jEq6\nfbSyGq/q/9lqW0RkBHVg0kBtYWfeLwJCSgJaZo623QXAqDHC5wJ6hjYAhEjq6a4oxPKZz2rb8A++\npADa9m2vBycgHB3f1zlsgq6aY35SfG+t9MsKVk0uYW4Nwv8PXlci0o1bngr8xpt63AHmcILQt4G4\nlo1SW6lPlU1Qv2/wWZ0NTI91CTc1/pasQXG3xJLgkFWbGK5XcvI2GrINtl7nLs8DqeIE2gauOk98\n+i6F8lMDNOhIZhWJREQiSk2ihTZ7CTSQ6ovb+so5FhFpxvp3C0uCfbQjj9GKS2zwM8S5sZknz5HL\noxTfUw+Wd30Aw9PR2LVmO4091Y/fWvtPRVF9sdbuicjnnmb/yiqr7CfHzrawx4jkUdBgMfQ40Lfv\nAJCJM3RyifSJd//ODRERuRyAU7/wCSW2XIN3YgVLidRTXnovOwGoloMQEcEjs3U31WZC1RkW6eTQ\nHaCOYKejqaEInV2iID3luuRQdBiRRh0EmCLzHmEMkofU2D4c547vccQO6z3nMnQIfuXXNdD67C+q\n509xzp2an9PDYwWaDvoK0i03QIMeoemjwDPXgxbR+IzU5mFfAcaNFY04XvuQL0zaB4B45z7UgzCG\nRRQUtZrwnEHHnjo0+jJQjF3RUY2FVYEGHUBfplp57WLXml03i4MuTm1ERrHR690CwWgJzVXbqNFn\nEZiIB/haqLMn8EcgMQ4ir1pMnT99TZDqSwAakvWb1LwHXulqpNhJ0Wp8gujVXfcAsATAZ7wyxMz3\njBFBhik9gnynV+/Dfk+5fWWVVfaviZ2tbn8xFTm+I5JBjTUoe2zgyVbHutGCMHK0q+mqK1eUHPLL\nn/qw22cVBJcBWmjXQBmtoTy0mASqsVOmbdR7ZBnUfCOSKfSRbUOSBNuCC4kiUBnq6vhHPX1/aclj\nF0zPDaCfPwEewHRgre3VgdOGUplNzPUj0kZ4Jk+hKDM1fn33wisfERGRX/zsZ/X7SGpBZFMPnv8r\nbRYipZgPJZ5QM26CIpswOVRP1fsNxhoBNLAO/shrmvJrd3157u62RhSdBvY5Vq9UR00si2viwMuy\n7Jcqtey6Y5DaagSqOTk1ADGnvDS8Qmy7XQv2qYPyu9TW47dbvK6cF7RiDxWFqYAMV8iinWbKCCBI\nhbqW34wA0B6etG4UKCWJn9V2Q7GjBXQu2ljVqGe/r6+jAAdy5w7MgD0AmSrOEDmGKb1OV48/Gg3l\nKTJ9leevrLLzamfr+W0ktkylBIrdGwYa8BR3QFFIe6qe5/qGCiB84lUtCFkPetGVoJ5GeApnBdRk\ngfJPRr64osTicsT38ATNGHHEetzc+OdhHevFBDTMJNd9m1g3drsaeZjIf09WKmrd6OrUthfhnRLS\nl70XtM7nwrOz+yy8a2tBI4rldU+e/NkPK85xiP5s/X19vbCi+/QPfZHRwS3FH/roIryAr6lDjbaP\nQhMJIqTWinqROm6NHjQCG/CCr774nNv26NM6lhborwfQ9D8GtdVSkTmgopIglSP6YKTUaGv0UEz9\nWrkACl4gAmAfhhzXLk7o3X1kRK5ZDIQ9hpiHW7fj8yRAy+sJypWBtfCVYjM2ANAtrl+UusnUfRD9\n1KHd1wqk9Eys1OX2gm4ToQSdxLI46KzL0nIX5bALFcquW5inLNDqmyDbEiexPI3rrzx/ZZWdUztT\nz19YK8Nx5tdyATItKHZoY/27ivXqJz6sa811oOWhOi1pmXzYuVduED4E8cSnZyjxagnLM38bdBAs\nSvZw06d9irxzExTdzqKi/nGg+GuwXqRAhJl7EteD/HKastQVWMKqevoLz1wTEZFl9Oy7eNl3jGmh\nsGRnWxHjvdvqVW7d1iKdb/yVL6J59Rl0D3qoaHzWgOgDqKdvo5zWBGIe117S7+oNFWv57ltKx/3Y\nJz8uIiIf3PIdgT73S5rpXVpV7OIv/7lKjNFVGswli19ERJYWdO6GoDITyU+onRVkgNirIUHUlGXs\nW0dKLTgbRZBTR8ku6eDdLrw6MQXQijstn+Egyp/gOpYO9wEVOEDjI3jpFFmLlBgOrjvpxLWavyf4\nnSwUIubCe6MIvHgDEQSLixghEaOgx2fEpOevn6Vp+lT03srzV1bZObUz9fytZlM+/OGfEUFX1TTz\nparjXS13fAm58xc3da3fgZTTFKIGUc0/8fiEZjENkXqiteH6mnUS/IxCjowICoucfhHo6uO4hp4f\nT+Uu8rZEtUcjX9Lb6ujaO0NOmk9laqunwfqu1dLIYWlNS5zXt/Tcu2AOJhAECbuw8JzXNtTbdsES\nO0bJbRKEO9/4ikYBH4JM2Aha+Xs7igF85SsqKrF+1Zc+dy9BVAX9Bz/9i78sIiIr60uYJ3/8Oubj\nVeAx3Y7iBX/zhop//uAt7TFARpuIuMXsEspo2WGZnIr9XV9eTM+4sKjetd3awBxwEU6qn1+U58B2\nEghk0JOSG0DcYGbNT+FRRGUsrHL3T+BNGR00mly/A+0nVwLXajD2AptHwDko+kJOQC1iMZm/Jywj\nU8xHSnkz3J8dRMC853V4PhNgf0xiHpVVVtm/Rlb9+Cur7JzamYb92XQs92++KSVaDi1YX3hzbVnD\nzGtLCPFAu60DLIrqGoIVpQ/lSZyZD3XcMiDQHhkDgKNabAaVWBtTGw1ATsMDQXw2shWTEKgE8Fdk\nOuZeL9DwQ2NRhrEWoSnDURuQS1pIW66gldg6lH0EoSX12PIg1C6gIpQgjM1BCW5dVHDw5/8tX27x\n//3Zn4qIyF989V/gbPS7X3hFVX9+4+//fRERufbiC/6MU1KMkUaFMpFYavT7+eE8U0nm+Re01Xd3\nEelChLMEJ0VEvv03uiS4fUeByqVFnYNVLHVWVn0ql+3XSJml8i/Tpswg1gJAkbc0a/4ZEnMfFsYQ\nRFQzM68M+y0B39jfcwmWDdSKqDdYOIQxIOznskxE5FoP7eSQUm0ApH3nntKv+yN/T0xIenJgIHQO\nAG4eHSkQmwU0cWop1mo1OX3QX3n+yio7t3amnr/MpjLafkeW4cV/7vrz7rPrWwrw1eEZUzxBDQgY\n9H4mKNYhOOcLP6Cfh3RI+HRkqSrLTMcAHQsWTiAaqU88CLmEVF4DwFtMSiq8R4YGndPCP2+pZ8e3\nqNglSOoAACAASURBVG80wfemgcdZWFHvsHJBPX8EEGkMnTbGLWVA/2RBTGxZSALCFIgetvSp0Ksf\n0CKcY2jIcX42nleiTndLI43dQBdxqUGvjSIgaB0aAE537vmS4TZUbxNcqynme2Ndr+Uv/MKndWxH\nPjK6ANLWd76tWrDH8GRHRwr0LS16+nC7w2IrBeIIDjJdO0GbbxMFxCDcEyzoWWYTT1yHOorKGnWf\ncmXKmJHMFPPPbcL0bIL3+EoAlsdowAs3glTc81a33QcB6w7ahS+AJp6X/p4jeGwBahKoZMQygG7k\nctCclJFvo9GoUn2VVVbZk+1MPX89ieXZzQV59ZoKT7yy6YUz2hlTYSjXRNHPCFprUxbtFL5FNIUx\nqJM3Ba3XwsPRM4j4JyrFO1g4keH5dzxUim46CDTsqf/GtW1ttpjDAB9YDtacNdBgByCxTJDiq8M5\ntYKOQAtoOZ0CJ3AJLByfKa1RoPXfggcYgkKbAQMYgOa799b33LZ2W9On00NNNVFR9sFd9d62iX52\nLV9sdOO+Fuu0UP47GaMvHhxZO/WehR6nh65KfURP01y9N7dsBvTbD3zgZRHxBSvb6Dx0Cy2nB0EP\nhHX0RqQibxSRdMN1vA6q0/Fe1pVkYzLThFEaMACEAHaGsys4PqIpvNLzM60n4kuSa+xgBPynAUp2\nDRqQWYDtuDJyRAcsbqJGYEhSivGegZAII1YX0WAsYaqPxy/Lcub9J1nl+Sur7JzamXr+dprIp65d\nkgvr6vHigJCS0dtBLIE0SuqTx5Qnivz6unS93LCeB4Jf5PDyufeY/JvbZFjzT9ClpbWCNVZALyXS\nShmvjMMF4lui71te+mhhjCdvZlmOqt7DrctSr94bI7qhxBgRfEtiENDu8cB37LFYXx/Dm9+/qU3v\nymM951agSjuuqUffelFJOAnIPffuqvzW7h4Q901fONQG+Sar0UOic8wR8JVF72XvQ4BlEeh+q63X\n1UKNNsVauQzEPDod9WjLy4ruG9wDCwv6vaOxn0si8iylprceIDobQ6YsChR/KQ5Sn8kA+LV5jDXx\nNPdjioErJRhvGyWyCXCDKMQHsJZPiPKzpBdYQtrAfTz1nr8D7GJpoTMzhmyIPpEDj9NY1w0Zoh6Q\nH6NsGCMX3l8iPjsxGo2kLCuST2WVVfYEO1PPn0SRbDYb0sa6Jsxfj1G+WUIya20ZBQ54kB0DIS0D\nNN5S9xzr+F5PveFkop4hmwb0YXiUEqh7xLVfwiwCUP+gpJcinA7hxXovqhEdxlM/6N9uQSsdQOhj\ngrU/0dqQcjwYqOeapuA9ACnOWbyEzEFc+nnafqjeutdXr7u1oYj9xZd0fTwIyqTHA+2IlKGXe/O+\nFg4VE913eKzr+Z07vhfCvVsQvoSXWl1XD92CEGaRe89irZ7LkPr6WINbrFeHQLebQa87euA6164H\nQMuB9Sw1PYpNdJ9zN4F+PrMvtDzoHMxtycmgp+xiTT5CtJAElNoWrmsL59xEd6W4NYvsi4gkKKxi\nkVdEGS+cIyuHa0ExUGn1Pq2hEWETkWMTx+0E53OE8eWO4zErzkVd/1DuLEKJdmSezpdXnr+yys6p\nVT/+yio7p3amYX8UR9LqdlxLqDyohmPlXActuDOk70hxpcLq/p5PBQ2QWqLazAiVfzmqBcugqaRF\naEgiigF41IQ2P1MqJHiIBNRK0jxJFUWaKoKCa5F4kM2IhpdcVmRjEJCglFsLmj6ScFRCa34C8Gg8\nwlgwB/t7vkXWcKDvXbyIRpcIZ12aMFD6nY50DGOcax3EkMGxzsXmujbjXAApR0TkcF9Tbw+3tU/C\nrbs3RURkBVoD16979d5sCo18aPQZgFSsbCRJ5/DAj5/h8AhLM2soVYP+BhPfA0FwT7ANNmvlY4Ta\nfQB/bGwqIlLD9V1C9RtTijFe1za1MjAOKbspgcUU50NKOUL6QK+hiRCdNHCSeVh5ScCN94aIyPKS\nfteli3o/3b6rtN6boPlOJh7QLbDUo8bAAdKoXG7FdX6PT+n1sQRsNpsuHXoaqzx/ZZWdUztbzx9F\n0mrUnWKrDUALAmFM75CUw6f7ITTiRz1fJz3og+gCck8EcITkiUEQWYwQJRiZrdEfMcKgFwk8D4sr\nmGpquhpuApZI3yUeEIpcv1I0BgUYNp0qYJmHhI6azkOXSjJsJ430jpTwQEE9/8qaAnsssMmQCk0A\nyK2ueMLO4oZ66xtoYT5Fh55uU2v22a66DNJrKyDWtECz3d3WSOz+AyUMvYUafRGRrQtoiIqmobsg\nGrVAvhqM1GN2uz69maFXQY7IqCRxB968XffbNnCOVNc9ONQ5pDYCyTdhsRGBMNKra5ifLgqI2gDz\n6gEIySiAoKPTg4DHTwN6r9MHwHgtlZYzqkKzEMft4lJ7i6DzXtzSOb58SSOuce6jzdv3NSqYgKLu\nehTgvmJUUgS6EzO9Dp7CKs9fWWXn1M5WvVesRGJd2m0a0G+pUQYH7do6j6B7T9pnMfbpu5g6fPAe\nhwdK/R2DfBIFBR9NPL25ziZ5iJROevdwLcXyXxJ0MocX6OcGa122aRYRSUDvTKZMmS3P7NPveVWb\nEgrFRvRca1Ap2rywjOPq2C4/c8HtM0bUNER77AJe5RhzGaXeCy6uQf33WNOBbHtO78sedJOgP15G\n1SKUMXfhoRsg4Ww/vOe23TnUeV5dVq+6hHLcDrwsr9kwUOfJ4bWHE3po6t6jVDZI/x73EI1hnwUc\nNwWFmhFAqNvfZEENruMCCEhM7RViZl5FfMkuqbpO7ccV9nhMxx2Ha3vMIdjnEuEPG95H6KXHdtvL\nSxp9bGzonO4d+Wh2lOl7+0foIemKrvT1GBjAbK8Cr0A0rxn5blZ5/soqO6d2xrr9us4k+jue+PUv\nH5QsjOGamTp6LH8c5j5aOECYMBzqaw5PTS9/dOy97NGhRgxEUTsLuqadMgtwgtouVXv5HqOECGQN\nwRo97LdSGnZ8bbpzFhEp0L3GSogp6Ougpwj7eKhe5biHohFgCUvLHo2vQ9W4BM05ggdIC6jJ1vzz\nfDyZ7YZTS9gnj1GDbpcGvRB6fZ3DAfZtoMuMxVgu1D0hZYJiqOMxdPVxORs5Ka/U3vPRyL27WsCz\ntqHKwj2U8jIimwwD5BtZija0AUljJVFofVMjmlZwfK7XWRTFiDJpzCL57Iuo48RnwBCaIP3E1OcL\ndBeJFVGUhfcPlZ69dqNf9LPkuUjY349lujqGRsNfsxqyHkNQuktQpcu57rv1gHjEwrY8L56qS2/l\n+Sur7Jzama/5y7KUPmSvSLUUEeEDi9TNFvTQqXjK/vbZKMjz4zjMMxfwzAd7ipiaoG8di4hy5PH3\noChbX9BilMX6bAGOiJfnWoVXatcgdAE0PiNFOBBPYpTA3LGx8LY4LxYhiYiUQNtZpMOntgXCy559\n/b6n7JI/Si+4vKZ4QJqgY0xQz0L6dNpgv4G53Dc+L6zPEEQovx1xbuGlkoyCJoFnWYKrZ7kpSoMt\nxsjluw0whdV19dY76CK0sa7jP+7pOZoAJS/hkbkmJw2XZbv2hPLVlNRZluGy/x6jtphSXWHnXaL9\nyKHzemJdb/Pwe4D/5LNRFcM49vKLgnkaAY+hLBkzT/1jxaiODz0mUs7RkjkdKe7PGiKagwO/D++/\nleXlKs9fWWWVPdnO1PPneS57O9sy7OsT7+DQCzvWWRxTwANnkLRCB96dbfXmmQmEC4EPjI71SRrh\nabyE9497XvhjBO8ZYc23iPXiKrrN1IHSh8yvtDbbK246pqAkWV1+WxpFJV0EgaKLzqIi7zboTEzv\n2qxphDJB+WyBQqUChUnHA38eObCDBeT1s756gNqirqG3NrxACr3bEth5bXTL6ROFBwpdBh1j1sCw\nnHQ10nKyaRDPLKcec2EUQC9FKfwIxU19dCo+CMa/toKMwBRINzw+ZakCsN/l6Lk+L5CtiOEFKfyS\nBDiHqc2u9TtgcDKvTymzUMAznuv8RHfLqlwbJO0JFURmtnSXx0gprBqIhZgIc4jS5kWUDL9wRfsp\nDPt+28lU8Z9GQ8d5PKSMGvsQIPpphv0JyYCsdPsrq6yyU9ipfvzGmCVjzD8xxnzXGPMdY8ynjDEr\nxpj/2xjzfbwuP/lIlVVW2U+KnTbs/0MR+T+ttf+OMSYVkZaI/EMR+XNr7R8YY35fRH5fRP7Bux0k\nn05k9+5NyQYKcJnMA36TDEU6u6gNT9k2GS2yUBBzOPYg3gBhfYKwjGDeEWiggyBtVGO4D0JKnYUT\nUI9dgI7eYkD+cG2SASwx3UJgLqzNpzGAo0INlxFMGzYDKiYzhg3EkiOMdzwGccSNI2whpvsfQwug\n6CvtNoGST1pectsuLCzh3DVEbLUA/FEleFHD/UmgW1ggrKeuXEll5CnBKg8+kmTlCUy6NOPyiwU5\ncdC0sg9yEnshMCN2gNr/Zy4947bldeRMkFpL8hZr6JOgdr5W41KByOcsfbiFsLkswwQtzoNUY9f4\nNQoPgT/NzGfk2jANTJJYEWj4+b4CbBar/6PePrUKRUTu7aE4LdfCKqYmqTzElCj31W1nC9BOa0/0\n/MaYRRH5jIj8kYiItXZqrT0UkV8XkS9isy+KyG881TdXVlll76udxvM/KyI7IvLfG2M+LCJfE5Hf\nE5FNa+19bPNARDYfs7+zMs+kv/dAaijjbAbg3RgKPSOkPYZOfVWf9otLqiizudR1++yi7HRvG7r0\nUHpxT90g7bGKIpQYT9L7u7pva1EBsgZAvXHmiyzoCQj4sJUydfSj2knTh32cSuzsp/QQIh6osexJ\nAG/VAOhGoHESqBeR3AFmq0v9NEr1GEcP33HbHu0qFbfEZW6Coru8uom5AC00oK+S4EIvNUZ6Khuj\nzXSgmkP9fEZC9TaalEJNiGSoMPtEckyK1Od6U6/naKTRR3/oU6FLK2jMCS8dObANNwcKuRzpSnzp\nLouCqOxj4tk0Xph+dECfc/l8eRQ84zXjPiQAsdybJeGhF2bhjUtRggLcg3bfYOgjrzfffFNERI6O\ndN6HUz1Oy3l6lpX78Yff+aPu2FMTkY+IyH9jrX1NRAaiIb4zqxDjid9rjPldY8zrxpjXj0ejkzap\nrLLK3gc7jee/IyJ3rLVfxv//ieiP/6ExZstae98YsyUi2yftbK39goh8QUTkyuqy3bn7jiyvqgca\nBikgCnPEeGJyDdgAnbQLj9Sf+KfkFCnDGgi2bLPN9eqFZ/z6kTmaPopa1i6o96t3dCwLK4pXrq55\nKi1JH1xTjVEizPUeBTuS4ClM8g09gxMCmSskEvFPS6fXRqYTBUzYNzDkbcALUkNO0O8ggnCJDdqe\n55l+VwHSzeAQ63fsU2+xx5tfk1NDkV1ymDJrwvOMAuEMps9IbZ2Cf9V03WygQBuWt8K7so9fH6m+\nVhcah4EPYZSwgOIoRkCutwM+D6ZUUqQ3mYYl8cuWs149DnAC6un70ALFXvFsFIeTxeAY2ekr1+T0\nwmGZben6P8z2baQKcdhZahHFS3t93MsYEo/H8uCwPyQjiiRJHJnpNPbELa21D0TktjHmZbz1ORH5\ntoj8qYh8Hu99XkT+5NTfWllllb3vdlq0/z8WkX8EpP9tEfkPRB8cf2yM+R0ReUdEfut0h7Jy7y60\n5gNhCwEOMAW6TIXTi+gnd/eOotpHfR8tsFuuo2ei2GXpgqKneYCSD6Gi21pGiS3ot1Rq7UC8opgh\nZ8wW9vBpT29essQ3OLuTPPzM2Z9AwoicSjAVcuHFKd0UeGYWjDADwbE5clKQgaAQBMuXLUgm+VBR\ndKLzScMjxyYGcaqv37OM+aJqLIVTRERWlqHXD8msbRCBpgBsSNLJAxyFqr1cr7dcUREzB37+GR1w\n/7SBc8N1TVwEENxH9NoU+khI1OL9hQKrEHt5jOfPUUQWZgbc8DD/Oe5X1xma18WPyH13hvud90YH\n0dQW7nERkesIrIqafnbrngbUlDfjWj+Um4tPIJudxk7147fWfl1EPnbCR5874b3KKqvsp8DOuLDH\niDWxWzZRnFBEpAm6Yh0I9NZF7Vx7F33cjyiIkHs0uA9RkASlqkurul6no94/8CW9nTXNFrBbLqm1\nq/iezYtaYBI1fJFLPLd+pNcuWbZ5QhGF88R4QtfmnsozRUAULYXgpes+i4KnKda47AwkIjId6z5j\nwy7DWE8WlCfzYzLOmwItJ/7A9SplvCbe28ZN4ATINOSY4wNwKkxQwFwU7IoMz0xRCSDrhyhYYt85\n/XIUJoHm3ABuwIzKNCj2SoHcN5n9cD0YSbPW7ZoNnwGiZ2efe14HinCSJxF6ziyfpRFzDhMvi+q2\ndVRfzGECTrOdO24elNby/imdOIx+xvlKA4HQI/wmDg+BZ+H4U9yvLooIcAJXxlyWDn85jVX03soq\nO6dW/fgrq+yc2pmG/aXNZZwfStoCKJP5r08BwHUBHh3tg6ILTX4q29rCF6wzZcLQlym3hztK+inr\n/viTTEkwMXTyFlc0DdhC1VeZUcc/0OMD+cZSSbXJlkwgWiDCigPVFgPddfJQzNwM14LWW6ScmhrV\negGU4f/1lO2lfXotQVPKOshP1EEk8SWUcKN6ENtEsVqQhBo++qNA92A61PnOMLdUO6YyERVoRUTq\nSAcyVelScOhR0G7ONtrUMUHxhhoAqJAcINw/PNp129oClGWoCBWktoKaTY374bEHgVMAfBahcF7o\ncWPSfqmAFKT6qPvguD7UaMStEAeNQGklwnvqRFBxx12HAGyOcH90cM1YvTnBFzaDVVENoKmdQpl6\nTJCQ2gugOgdtw58m1A+t8vyVVXZO7cyVfKxMpUSDxwubvqBh+4GmNHYG6rWT2qzeGZs0hgXfBFua\niBYegOa7i2ihvrzgtq11FRRst1gPjUag+J4ClF0TpOLYsSdjHXkJ7zHXUjtsNR6RqouHMVNB7vPA\nNRO8Yf8BgkkEFLlt4KSkcJpxSAOStgrwLQ40/hdApmJ0MCTruUZaLot2vJeiYjGjjhZaTqeIoupp\nMP6hetx9gFNT1J7XqVGH1GXv0AO7gz5BTB3DLpRwdnZU1zHLvBePI3pkKBKxQAaebmtjA8f0BVyc\nqhRpNKr5tppQwqmRZPRoQ1YCfr6+v5jZR8RHBVmBKAdRoS0IqoKMEzTYHCNKSHCubArL7kVrq74g\n9oOvKJ1m50jn8gYA7wnuvQnTy8F95Nq/Nxoz99eTrPL8lVV2Tu2MPb+SJJh+GfQ8VZdPdZYm0NPX\n2Q0HHvVo6GmNpNJSty1DuWlnQdfxcaDqyq4r/G7SIOdpuOy4IiJSoPCImuxxwhJPrL+cZ/bP0DhG\niimeJQjx6Rw+sblWY9RR4KlOnXqm4sICE+re59iW6cDJSM+92fKEncho+rKNgpsEeMcE60j2eMsy\nP6eQ4XMREkt8h8AJto+9dtwYlOwMkQXntg5aMrsuhcQmXld2MmKaa4Q+Ddb49esYra0ZBbZXcD3h\nOXsgbi0uePVhqhTxKD0UzYxB625CMWqa+lu/QcIX0s3ss8cOUCZ/dE1NhSN2nTIkGpVI5wUENjNX\n9DNhIY4lxuPHQnrvyy9fFxGvjDzeHczsm+dhqq/mjlPp9ldWWWVPtDP1/MYYqSeplFi32qA8tI71\new1rzAk6144yFnOwi4pHXht1avWp12MBTgLvl4Q91prU61fXFs8Td6jzHtBL+VSPMU0OVcU6nqMP\n6ZWuz9tjXsvACzqyB9D9KdaN1Ikj1TjU2CMeQFIJC30oRjsZ+cKeh/e14roNPb56HXTclkYCi0s6\nF+xjJyKSW5TWAkEfIOsyglZgb++h23YRPQRSeMheH12W2MeOqHngY4hrNDlgy8Ikfc0CEhEJURRi\nqbm+CXo9qKAbdvaNQIWmkEgdBWEdrP3r1O4LrjNxDpaVx5HeezV2Ugo0/ll45Pow0MO76wKvHvTS\ny3NEtTkLfOZ76wXHx33Shc4fqcDzZcFhT0nefnmeVxp+lVVW2ZPtTD2/LUuZjEa+JDbsjkN5JDy4\niDYPgQ5Pcq4rfR6eBSU7t1TAgnRbIrBR8BScUoiDOekg9ywiknPdnfsnNqXEuOb0KDDW+oYSXUF5\naDS7zUlzQON3FSXLQIkcsycg1oQh3F/OCow0qUqLS9kfek5Ahmc7u78I0Oam0XkjEs5+iCIix7sa\nRbleAsAYIlwzds8REWk29LszUIDptdgTkB7fBJ5tCqyiBcouqbR19p0PsiN0uCzTJbbCfnlOMz/y\n9wSzN1nBLjYotYXXZW+9jVWfaSKlnNeD+0iO8wp6PtKjZ4gSChQ6FYgWsgkig5AS7O7DWQkwFjeF\nwh/keNy9dw/bsgMUshXMIuUhzVqPMx6PZyLLJ1nl+Sur7JzamRf2RGK8uGXoMal3znz73PtjrHGS\noAiCBRMuj4pcbhsRQXvB5/lTeDmWcnohRgo2PGrMJhCNdUIdKE6J7Kw4p257MsrvS0q9Z8syouHw\n+PQA8PjsrMNXEZExcGzHQQByn1FoJGR7gWXG40foD1Br6Gt/ouv546Og+4vrbgtJrhGLUiB8OSP5\nBWmv3QhjwTp4tgJ6ruYZpcEsiyaTENclDTrpsJQ2iShdxQIcMCCxni+DwqEE2aES7L8cKDzXyIyY\nbt3x3YbbiEIWu8yKAH9y3AA/fK71S9D/auiKnEFkJmNH50Drn+XqrtBqrgyb10dEZGdHGY4sijrA\n6/ERsjy4j8K+A7S06tJbWWWVncaqH39llZ1TO/NUX5ImLssyo3AKQI6El/mUBQkSWaAhl4JcQgJP\nDa/WUAEnTDGhDhoFJFnOMGq2cKIepAep4cewn+G9nVsyzOjBRScvI7zmf5gCohIQz5lqMFDtRWjf\n73lAbgzSSkaiCI7RQLhLdR0RkYgpRLdE0HO/e0cB0gxjqgWD7YKgMwSIR438AUgyJPSI+IKdGnQW\n7TGWCAV18LFhcHzOIQt8RiNeB7T5DsLZJhR+jQP0CPgBiE318zw8PhSQU8GYAKo12PST6kZ+F6fN\nOMErl3cJlINqwX0Uu8IdLKHA97U4rxjLgHrgV/MSikCC650xTUvQ1i9ll5GKpn7gCKSqONJzdmF/\n6glsU9Cza2m9Cvsrq6yyJ9uZF/bkZem4l7MdRvQpOAQw44pN8CBjqi4KFEwyvJfD0ydMPVl2jPFe\nth6hCAgFHTW8pg2WnVJ5J0g/8imK19IVFcGzAWcKIwxHF2a7baf7x5bO/oy5n+szQLAIac0pvO90\n4tVtMtJUSUld1OKdKFGwahIo4RinFsvCD0QwiATqAMqGIeAn+Bvz04cG/xG2uXzRdwSilxJ4bxY6\nOY18XN4iKHmO3AQghQXyFrsf5ZknwPRJoMEujQzRmdXrXEeXpSQo9spxzgvoB0AdwxLAH/sQzJTc\nRoxUCM5CixBU4bwI9PLw2oC3rhMNhEd2VO2AEjwpZsk9vBcc2BmAg5c3VXHqM598Tb97rGnab95C\n4dsBUt+BlmKGop+D477roXAaqzx/ZZWdUztbko+I2Mg4jx8WJ7BUlVRLlnxGwp5oOtRxoL12BArq\nlMqmE12DGhTg9PuBxj+e/AaEjg68bI0eB2vOsHuKYB1dg4BIbKnMi7U+I4CArukKhOY6t5D8k4ZU\nYEQjJKAIjlMg1ZRHOK84ILE0MB94bNPDFOUQc+E9gmvnDCIQX0vsXCJMyYbeB4xQrDNFT8QH20oR\n3kT59dWr19y2Kc7p5vff0jFgHUwsYMzUX0Cl9cITs37HU6S9F++jcCdx/fUwl+yWQywmmB8LT59Q\nt99hSRgzCnuSOCB5YdxUJqZIDAkzFHrR8c9S0zMXDDLMmVXx1THkM+85hWfcp+x/ICJSDPW+WYEe\n5c++9hEd74qmJr/1XZ3rg56/zj3soxNTkXwqq6yyJ9iZl/SKBAqqgecnuYe0Rfavs64pGp7kY7/P\nCO2/SJyJEQFEjRO0zSnphaf5EOu57UKfqO0FPcZa4BHqTjVW36uD2EGCEMtqZxB8ltriu0leYRHS\njBdnp1d4ANJ5SX4i/JAFJJ8C3XaocjtEiXMtfVQJlkIoTnce0Q89MdfDoRb/GGQVRiMXt3SN/9LL\nL4mISDeQ8dp9oHNHqSySVYqSKDlfw9sM+Akiu2zK7rMcm/dHPBeWHrN/wfLqBvZFBqj02ZDFJe29\nyE7EI6zx2T0oNlTz9d9D9eGcxUWMJJ2cf4gpzBaA5fC0FPlgVBKHYhuUFHMRBkVUeP8EswOpL5Ym\n37mjPS7u3HhbRESm6HJVD+6jCcRGplkuleevrLLKnmhnLOBpZTgZyxjFJ9RYFxEx8PQx8rzsGUdx\njcihtkHfc0FOFx6C67EWqLztRU/vjet4+iKyYLELBTw7bXR4Cdbk7AQzAg7ADAFFRh29OFhnE22n\nJ2N/dQFCnQXnTBFL63Lf+hk9/gJEKo4OgjJXugl4SBZAsSLKBPJRLBCih6Qno+dnV2AbeE466TpU\nPTY3L4qIyBq87TDomHSIDEADwqZUK3WlvCyECkt6Hd0ZHpTRCbsbFwH+AMyAUU0DdN5WW68vowdm\nDERCuvZsQUwdmSBXLh2IbQym6EQMr16HkAn7RmZBZsBYRA4U1JxSvIMRAfL+gQee763AcbucfJCa\n532zvKwRzPPPPSciItsQpd1GtGUjH6G20LciiWtBNuXJVnn+yio7p3a2nr8opD/oy3SC8tqgLDFm\neWbMnDpFNpALhRepB4UllLuiR8jAvqLUURpwAkow1eIU0QHX0Vy74VihZ25alL6m9JA63mMIhDqR\nj2DNz8gkgifOsEZnRED2mIjvwksPzC42HJrr3BOKgMKhsICFstNcr9qgrxzz68xxj0bY1rEodbss\n6IIUoWaU0tZT5q3hre7du++2/dYbb4iICIImabHQhrLlrj1vkOefK6wqXY4b0Y/xkRdLbVsQtlhe\nUqFLevrhGBmV4Dqz862ZK/9N5kQ8plkgh04hmQWIcpJvMUCxVMAjoLfkGr+G61mAU+JwoADPu6Ld\nfgAAIABJREFUsoEAqEggr85y3YDVWEMGyKB0m1HOtcubOL6e853tQ7fP0RgZHxPNSL49ySrPX1ll\n59SqH39llZ1TO3OST1GWLjAxAbhWAyWUXAkCJty2LGZBExEP8I3Q3LC+gGIO6t8HRIvIzgKGx0ca\nNlGptdVWcK3ZCUJsp+irYXEdmnUksVhQUacByJZNmL6ZbajI19UVr9Hehbb8gFRQhJdsJ767o5rt\nZdDimvRjzhxXEZQUCNtJE9wsndKLHmc6oQKsvj+ZeF39JNGxLDaUNkwwlcuyULugCWCsnqKwxKX2\nZlWSwn1cxx7MHRdBXCKETT2XV3TZtQzCC++FY1xvgoRx6r8vNbOad6kBDRfLRdfuO1ByqrXQeQgp\nvz6aYhLEC4N2t5JhGpYpypypRDZq9cu7zDxes0/EF+sIzkrEFzOR/NQCOWltRa/L3pEnow1cc9DT\nh/wileevrLJza2db0mtFkty63mXhE7BGdgSenCRPkOxDUkUU1J+aDN4czpoOjBWSg9iDOmaiT9cm\nPGNnUb2ue1JTi7fw4NdoqOlA1yWFqcU1dIFZUSAqCTxzfoC+AyCmREgxdhE1tBYDXf0aB66e/sjp\n5lGdRz1QFDzQ263/v72ri5UsK6tr16n/qvvb3dPTPcPPGFEkRIXMA6APBNCoMfJGMCGZGI0vJiAx\nUSY+EN+J0QdjQjTGqPEnSITwoOiorwQUo8iAKAMDPT3dt2/fe+tW3fo/24e91t7fuTN476RjdXVq\nf0mn+lbVqdpnV9X5/ta3Vrjynw7CY0UpdmAyIU3TWmJhiee8JOClXiiqIi++KWh165QL53b01cbz\nY/6d/MXNx4JHnk+4TnoptebEJ2+nsxdU4REgSAU/FUJrxotrVHgiXT+2ZVURjVGW0VqoFeHzk/5h\nY96oPLeg6+4bz78gyGnC9q9n67bGL1bHcPyD/59ytLmu1+F3TpGlhfcWEVylwS2xDBEgZNpzXpyD\nHOrqsM24x6i2qAWYtUBAYS0hQmwUtYq600WWPX+2bBtqq/X8YM4UPYFxCedZRwWW0P0Ch1i+dQ1X\ncGRVQy1jevHXbd+Mz90iaKXD1kmf3P5Sa6nLa1liDkE3GaFo+OhsRMZWAlR6ndR+3NsL3rDdDu8j\nGG6bJCHtliFhEJT2NIBllhNCUSN7rHLz5NmwYHuQj83ooVuMMGydo9FQ/sv9Ub5LMI5gq5YAQuCq\nyKFYUx0ieMXj4/vxubpvi1x3Te6DQEv6eCeGHVivLMjveSWjRicBs8TNd8ooqsU91WeFFtuGFQ7F\nqiKT6h3SK2wWUrs1bWC1/aSzJ70AcTYaFaEILGItJNameB61UkrF1sj1qHVr9JmKUGdGa1B1mAm/\nYwuuu6RyUp+f89t/+IfiMXt7oY70/De/HfUGL2PZ82fLtqF2Kc/vnPsogF9CuKD9B4BfANAF8JcA\n3gjgWwA+4L0/+h4vcWnz5zx+IsMgOMOo/EyZC45FWkGAiDTjpgb8IY4LjXQKQHNGUEirQz10C+ho\nBE/cZp4tznepBtUm5Hu3dQhpEnip2gZv1REop0yvP2VVf3ISvOmcpBiKcurMFZemm1B45aGkeaJn\nWJT0RN5WjquDNa1zMFk5/Fkj1UZKRlGqMktvbxxhyykK2SLb7RbrAlMCW5IOIoea2mYwhuOxzhG2\nLTCOcMWmSi6dAYcQtR3fD0q+AyrYaohnl1DYcAICMoUoR1Vy1XYmHAZbLhIkuyb4rliTCbpZeilE\np8hlEfn/pT2h6IMRTeOVWg7SdloUrB3VpApcHfsGEk3blMCrEw7yKDqZkMZuZj7ma7shim29+fvx\nxa+8jMvahZ7fOfcEgA8DeNp7/1aEb9QHAXwMwHPe+zcBeI5/Z8uW7RGxy+b8dQAdF5KULoCXADwL\n4N18/I8B/DOA3/i/XsQjePLz+R6Q+rKRJFOen1dFPdP6NXGjj9VT1/QvHx8bWGyTF+8JFYBQG1be\nr8FcXNRQgMn/6U0TMQThsvTcCwMJ1qBNoxDpRq1yHiOjpTc6CdFGTUSeZtgEAOasASyNZzg+Drzu\nE0YJMx7ToIdrmWp5jcMfJaONDkeUEwkG82KrQlurVt+ViytaODU1kT6r73VCW49IrqJq//5+oKQS\nwWTYCA7CaASWaXD0/Ab7odx4zAhPRJeCQ0uX8KWztKc1etXHb9wAAIyGrBWJjJUvbxG3SakneFWd\n4YTfn5nZH5WcNH7dVCdAmno8L0unpe9A9Ph1s98A2l1TxxKhCFWc5uw+afBMe3Dn3mE85u7t8P/j\nwSgOcV3GLvT83vtbAD4B4EUAtwGceO8/D+C6915A75cBXH+1451zv+yc+5Jz7ktjEzJmy5bt4dpl\nwv49AO8H8BSAmwB6zrkP2ef44KZfFV7kvf+k9/5p7/3TnVdRGcmWLdvDscv8Gt8H4AXv/QEAOOc+\nDeBdAO4452547287524AuHvxS3mUvkSjeGXYLz6/c5292EoRx97MhFNThsNjFnn09+ndMPt8Nk6z\n5z/wVGj79dmWUgFof5dFKzLRdtppNrzPuf1OPxT8IpecWnBe3ABG3JPhaye29kKIOmJoeTpIaxox\nhG9TploFMhV1xBpjJxkVVmoOvtlQmE9GHDuhR0HONvkNVIQ6v8dWNk2tyX4vHPvYtQAqkfimuPQB\nYGcrrGs8FFSaQCOmYWJacgbEEsFIC7YZ9QA/D1snU9tKXP4DMtl2WBSUVPfu3pV4zOFhKJ6+/NIt\nAECPLd3trbDufcqS25auJM8mTB88v2tL0UxbQdmiqNylr2OC9dYrz7NPEiuVKwhY48OdXnr9+UJS\n8tQkWITUanEavjdthv+PWwh1wQJ0vYl6/UVc1i7T6nsRwDucc10XEsD3AngewGcBPMPnPAPgM5d+\n12zZsj10u9Dze++/4Jz7FIB/RXAvXwbwSQB9AH/lnPtFAN8G8IEL380HbyOghIcBT/iqcKNksZf0\nDfNYfEklvwlrCKolqGGlVk1pPI6KTl1CRgXSUKFJfGrHR6lbORqHY7bZV9nZY3GQ0cPMa4bbwHvp\n0RbL8LqqRalYODWKN6m1RG+qdhQ90dXHHg9PqyXPsKS73iKsVG2q4XEIvCzgpVYX8zHXTcYXaSII\nZtpqJC/iCAUW82+Pw0fLYVirWJKABAAS+ElFwik/D7XILJNPUVQLianNRc9vvKwKZUWt+ljB89mJ\nTE0pgnz9618PADg5CZ6yTy6A2Ti00A7uhn3qtlNLcSEmH4LEFBTou+dMOHJeLFTF0/Pim1bFqRRH\nQWQZUrtToqsmslBEx4ix02NblnurwbZZmVqV3a0wlDZZusoQ1UV2qSTce/9xAB8/d/cUIQrIli3b\nI2grr8A5+KibZyG9tTjAQyiqrnB8XO28M9Prm0WmmqoEMudW0GsnL3WVQzhNcfmRcfb+/QCKqBOM\nU2+lVl+zzRyQr6+CZasRrrQtgnuaBv6pcdlGEV5vi55HEYU3baOGhkQgth+yDVFtpt3b5zpSzt/r\nh5xvQm91dC9wuon7Xd497AdZiqg4ozxUGgaKouwwSJM5slNEtJQmXZQiis89ZquyZPtOLdFa1ERg\n1Ga6POc5/DT8IzakpmlVKnLQSPXuzhbfT5GAOPINmIsKQzXJns+Dx18QSn3/MHj+aTftk8aka1D0\nybFjva4dvGmR348sTGIBalI/UJ53btrMtZra2FXFpwh4Mg1sgcAc5d+Xc36nxY/IL3etkY5ptsLe\n9Xq91+T5M7w3W7YNtZV7fg+PksAUV5pckNehBa/qE+alU+bFZ/Tm02W6si14vAZvhCLtk2W31zZ5\nVwRNBI8pjr0lYbiRAdUAMApIH5D6aINQnS85wrrgFbtuePla9Ax1emtPD9AnwGY2Nkyw5BUcTtUB\nCN7k2tUAmdjeClVsO1hywg7B2SREEtPJgOtnDcAO6dDDiPRkwtHhhQBU0qYzHPC1NvXv6NkEmplp\nOMd4fg0OzVSv4efajDx03B8DqJFWg2oX8lTq9jRbySNrEEkes81OjOo3JSOChmHvvbJHzUKe6wvf\n/CaP5fARF3P//kE653O3MZ/ntiyNlt6E0YjqVpMJaySdcK79PqPCdhpQWixIvOGkOCSmYikDpSih\nw8jzyg7fb0TtSu5PPdYfzKbq6+GrtYaLLHv+bNk21FZM4+VDrueV1xjYZGSSJVSXOaAgvFHRx+SP\nljUXSFXZgp64ZcZnYweAdQKp9jbo9URT1TDqMqJ6EoOqY244mbN/TY80Nbz9M3qjkpfjo6MAvewS\nM2Dz0+PDUy0cAHDzRvD4TxKaqiGSe/cShGJCj398HHJ9QY37V9P4skznJAbeOXN/wW/r6pfX0jmr\n/61+foNdkaidaHLKVjtEM/O5PFtVVxGxG5A+J9VEWt3w2SivF9R4aqDS06k8vyrr0mnQYBJ1E8bp\nmFu3Qn//5s2wh1cIMX75TgCjDk5DpGSjkVJqz1yncA/1Znj93vZWfG6/TwIXaT5uBRddZ+QYIdPm\n9dNoMCMtdTr4ftNxouRSjNHi/mtcV90L7UVhojXnVJ8pK+PZF1n2/NmybaitOOd3wTvEMd30SPT8\nrALP6aFFdaT7l/aSyv8KWSa9+0b0/K9UctEAUYwkOGY5rUtR1hJDUAmIAx9j1SpEPNGUxl7KCU8O\nwxX6+H7IzTusnp8OgsdeGJyCCD/29gOK7knq4omXbDi4z7UmsocB+/mLabiv2VR0ozHatD1a1oLV\n66hpSHSYIgMP28duVW7lVaQxqOp8WBcHYeiB28zXa9xDRW92MCnSsYlok4+pd780ZJbCAAhzoIhC\nOb5uCxO5HB5yyIVqQl3iFGIkwwhwbsZ0G3VV+6vYA9V0rOT9CenT9vbDmpqMLntCgTIiEEFpeD11\nJ6rEJYpcu71UHxgch/UrghSBSXGahpcAoNU0dRqnDsMiV/uzZct2seUff7ZsG2qr5fBzDkWzgXIu\nwULDwS/y3FLtqfC3hlvESW6HRFQdkfCkwtspB2PmhslHhaUWizhqXYnVdUYIb6+f1tTvk2m3FEiG\nBSe19gTvNQWt+SwUbyQyWS5V0ApbvbO7G597/RqZaHjffBxC+YM7YVBG8tsngzS7PSDfn5aw1d2t\n7EthBkpmEXar9Ef8BISiMlwuK/Bbgm547HJe5VmwxcHZjKkYP7tYHJyJm19MNSktknS2PvuCrysJ\nLpuCOHL0bW9vV44ZRzYevX96fQ1jCWTT4uvevBkKokMW/EajxH474tCMOPnFydAjU9HSpHWqUev7\nOKLoLFhA7jnx9aWU0xMEVUqwU4A2fdUMfLjBYqZEbKVt0eN5DU+HPGebViT2pcuX+7Lnz5ZtY23l\nij2LpY9XKusR9F9JK0955daYri6SM1OoURDQZTGnRSDHznbwLoLWAmkgY84rpuPrb/XCc67tBy98\n7XriJFGxbsz3LCJvG5lyl+KCT9FCW8AXgnlOCCZ5nJ5nfyfBhx09wr2D0LY7oQDogGO/gisL3gqk\nglK3H1pAnRZ59gWSmqe1KBpQ4bNFT3rG4SJpIshjh+OXldsz8fX3w/vUYFutZ5XjPYuoanOqNVe3\n8FhJrfPvmuCq55hwgNS6nXC9bQ5lzchWc3QU9mmrnwpmiaUo3J7S0ys61N8Tw/6jKEfoW0UWZxxy\nkje2a2p0OCzFz0MDVXH7DStTnUXGpiTR6yqihjecTUyRTgVRbVlkBQ5/2ramTJHdsiwrQ0gXWfb8\n2bJtqK221ec9yrKMwzvLV/H8cbCH+fpMUFRGBBbKuaD/WMbnhFtpyKl9FN5aUNNwylcIod3ZCS0a\n6e8dHyfp43uHoV0nsE+PBBen9NBdstbaMdoW2Vt3SA6iCEbcazMDCHrppe8CAIbkbU8ADZ0Hx48N\n8Ejjt4XniKc0C8iGayXGXaGcnseyLSUCkKkgu0YWu9Gk4hCjnSVHk/u9sE+lyTXjc1jz0ECPEk/V\nH2qmPRg9e6nhn3Oe3ww+qR04pcZgi0CablftTY1Apz1tE5bcY5SwjCQq1N+LWgIGmMXIIo53s1ZR\nkFijNIzOAt/UI8uwCDrEVMy2pPmezuaSDSfDryLIWLSyPW9GTZH3L9xdMhIbnI4q5xXWQlKTnWal\n5nORZc+fLduG2mpzfgeUNR9zrLlRlJ3NxW6ryihz50WVQXXs05JVCV2WquySHVUpmhmYqGv4h15o\nyNHPIfXwlBdbmipVps+GwcMNqFazzVpCzZM+yVxtVSdotZjncWxXJBl379yOzz2+H15Ph/cJ9pCn\nrEcueAu/Dbe1ujwwoan0DKUZvPHsRnRIPqJhF43TKldvGBXZgnRdE97usBYiwMvRKAGOpvzMNDot\ndRkFEoUTc3E8BE6KN1ShFSuw8ngsLIhLx3NohqpLM6nXUF13WJqaCEFbqsK3Wz2+Rvicz8iLbweg\nBDPXSG+DNFslo8XJOJG1bPc5Zl0oigrnPj1jN4laD61u+swacYx5wbVwFDlqU6RTlmp0ncCjqErN\n6LNNqPHoKEWoJaPkTrtVOa+LLHv+bNk21Fac8weihAixNbjJ2LPVII+ulnw8qvaal1M+pEEMVWtF\nmdVum8o6PdkhB22mHJS4ek3aevQip6n/e+8gVOpFZaV8bslK7pxECzUzZqxz29rmwEddVefwfoPT\n5DnlwXrKYV1VpSjq5RWvhDSXUZkmeJwR83erC6/8t2TU4Xiw3lfv1+sY/UC+nqi4RJKpbgkqUGDh\nHbgmfTiqOKufbbohtXO0V/q8UxU9RV4TKivvXgmdjSY1FQpW2Mfk7bfqNWPqMgw4+qw6hyrr1x8L\nUOqDuykC0x42+R2oUY15SphyzZBl1puqrPN7yihWJCQD1m/uHNxJr+8VhYRj+6zldERcYp21wAYq\n7zP6aBDOW5zTXAAQAQPT6bSCnbnIsufPlm1DLf/4s2XbUFttwc97lIsyztTbto5SAN2lsGrulQbw\niWbFLnKisT0iJh8WkZqGYcezMFYwlJNM9WhAHjoVTUwIfO1qmAXvM+wXKElTfC1O9ZWmYjPnpFtR\nhLBfIfYJp8ymRk5J0FYfk5lqyKa0xnLMi/k1Fo/E1sO0xnIciPd+dBBC0S5blQr/R8NXAl4mbKsV\nmpxjKKzilwVmFSxOiSVJEacKsbHSZ3I1pSU+wrg5Qy8RVCOX1t5Say+sYUHh0ZKh8ZBrbXQSmKvH\n8HhvN7x3p6Upu/DcE4qinhnZNGlCtBGe62MayelQk4poik+t5/ssvHUJBR+xkCzWJABo9yQ4Kq5D\nfrfZKu22DXty3DrtnWTByBXIz6VmgFP61hRFkef5s2XLdrGtnsPPxxpGZSAjAn7oDcU+IxbfJa90\nC8v1z+LaDj3DDocfWpLonqSrb7PBFhPbXm0OYggavLVFdRvj+XW1lfeeTAR8IR/gTJLXyTNofnw2\nIWtsqTaSGIutaGW4aTQkTnpOwFHim4aXYBZFJcNazkbVwtDCzJFPnTgRwnOOj8J5dM8NiUymKRoR\nX8IOGXDEMKxCZt0AjsqSoBgOUolZJwYH/MwSKAcYDkc8NwKYFD0sVdgyxTV6WXEQjlikFYhLkcxw\nmIq02sGSnrekys+M3v2EBd+mmYdvcNhLReamWJ3EyGuKqAKZTVnsHXPvBmwhdsmz/4anvi8es7e/\na7cjwsLVGvULo+UQlas4WMW7xYcoiLmNNgWQKsvye4jmvbplz58t24baylt9fpl49HxlpLfK3CP/\nKLUTz/xVksUA0CNXvfjcrxCq220pLzIgH3qwBo+pLcQ3p7YUgRcmZ24RwHHtSgB2CHwzHAYPNB6H\nfG9m8rteTyzAbHPRQ9C5Y16zl2aNeMrrUecvQkh1RX9lHp9GnFkrobSz9eJNerS6wFCqq7AuoWOK\nispP+P/+fmivqQWqYRfLmlN6gXCYI4sTkC8XP2fjY9Tdnc6kDBS8u2oBnZap0/Dczs6C926zhlNw\nv9SWPDtN+odNyXiTp//2rQChXvLzVWRXibGcjtXQFMeCyyoHIhcaXo+j4IpU6o4gMUYlLbI1h/Mn\n5Ff8ew1B1ckeZb4/c/5fWgdi7Y2PC/ZuYNzSLlzM56Z+dLFlz58t24ba6nP+pY+QxdLk/MoT55HL\nnJfjurwKWWV7aclbrOprKKfUFXpJ0o1u8iJFXR6TY5oRJkzVlEiHntZ0ymr4GdlV95kH7zOHK5fh\nfUfjNCSiYSJPbx3VYATLNa8voIZ45boxlw1eY0jAStFIfkp5Lmqs9nO/BATq91P9oSVFIO6LCsFS\n8pGXsio5AtnsUr1YnkY1mYZ7NX9RhVlD0QkjuwH3EUgp6YR71o5waAKojO7h4Dh49PlCXIDMlfkZ\n9qletLedzvnwXsjppYB8+1ZQrb1PdaHHHg/6h9d4C6T8WVFPHPuuq5aUugkanGm1pDDFz7ch0o2w\nFjs4NJ9RTakuzsHw+p51m1bLDuOIK1EDQtxLakd0uV9SH+YGAQB2d3byYE+2bNkutpXz9s8wx7Ih\nPvd0lZqKNkpVfa+rfLhfHq9nKt9brPK3eRVuSuecnnJmxh7Fz6/KeYO5m4ueueqFgdTPV4V1zurs\n4Ch4pyav4N12qmbHYZRI1aTRVZI/1NP6eyShiMMiqnxPqVvHNduerqcn1j3SOeiTlKRl1jLk+Kf3\nqm9U9Qy2tjhc001kGILQOh6jirQYkZ0ZHFIEIUbeJTEMyofFYLs0MG51BKQULGKUDnPkY3psADg4\nCiQnntXxOuMGjTiPqHR0Nkn579mZdA9DPWYyCc+ZjoLnv/NiePyJx5LnL6TpqHoG83dH79sjcQoA\nTJfcS0LHO9zvBdd4yA7KliFtaWoIKza0CCOWWpEh/oBUrNRxaDBqI4RcBDWz/bSmw4PA6Hw6Oasw\nJV9k2fNny7ahtnLPP8cSS3qVmcl/heQbk/xiSa8yZvXTcczyej9dsbfYB1cdYHs3XKnbbV41DTKu\nSY9b17isKuHskXak5WYorVRDiBrs6ggQhtigxnvH0DwpzxUq7IzVd5FJ9HsGwUavUVdHgzDGOc7p\n1hnQlkY2Vfl2zP2WjDTmZspFleiGSDLbyvFVKyH9Vs2sn8FSnQrE8rYlq8tL03nQABU4EqzOQ8lz\nlk4ADBmGjq83pJ0XzkPV7eNBqtwTYIc+FZOa/E5MhE+gxz8dJMWbHrXyju+H/T46CsNZ41FY0/7j\noSM0OLwfj9l77IbekWviCDS38uAwjc/u7IZBsG3y9E/0feViFRHADAO1WZto1dkJYl4uby7CUyBx\n/Ps4yCYF6mD6/AuDU3CKVpcLvBYGz+z5s2XbUMs//mzZNtRWDPJxcL6G2SSEShMT7iwFUVQ7ioW4\nFltyEeJamX0ON5JwFoNtnCexYowKpwoBLBSyMx1gaG+5/ucMXxtqP9bFwsqwzaXQ7hXG8Kwu6h1X\nbTXa9xL7jFpNieOQkE7DRehSv65yK3js3ABSBE9tc+hELT0N0eicfS1tVINtU6VDEw7ADMlzPxlb\n1lvKTXE+3RP2PBqFMFzQ7JrhCIQYfLj/krxejENh0Q4ObW+HotYOgTkHL30bAHD79u3KeexfuRqP\nOR0SwMSURHDcAXn0RiOu3wzAzPlcFwtwFNBkAbO/ndKiJkPs4UlIBY5YVFUYvrsX0o6aN8AmFmUl\nia5bnGP0AczHyv9ogCjCefW9MryI4gkol7XM5JMtW7aLbfUgnwUwl7KO8VILMcZ0yZRCL7LNAtm1\nq+HqvmeGRGJhj7UPtet0VbSAh3ohgIWUZ6pXyMghZ67CAg8JHhvlu+mRBXkt7dWWHtNHmiFx+YX3\nPx6kIRSBYxosBPm5VFrI3EpgknfpYxK8eTxhu1TqOywwCSwDAFO26STJLc8vthedc6ubipwSCT2j\nJ5bCzQlZjZ2Bj7a5P6NheGwhNl/uoRhsW6Yg2qSst7gOE59deHxv90paijgYWRiWdoAYl6VvYIuQ\ng+ERrF1hVDAehnPVZ1Yz/PaROZhrmfDctbdLo5tw+9Z3wrkS+jtm9NrgvktDYGfbgHBYCD3vlMX9\n6Iw4rNihkraFwtgqy1NhitnSZ1guywof4EWWPX+2bBtqzr+WS8WDvplzBwBGAO5d9Nw1sqt4dNb7\nKK0VeLTW+6is9Q3e+2uXeeJKf/wA4Jz7kvf+6ZW+6QPYo7TeR2mtwKO13kdprZe1HPZny7ahln/8\n2bJtqD2MH/8nH8J7Pog9Sut9lNYKPFrrfZTWeilbec6fLVu29bAc9mfLtqG2sh+/c+6nnHNfd879\nt3PuY6t638uac+51zrl/cs591Tn3n865j/D+fefc3zvnvsHbvYtea1XmnCucc192zn2Of6/zWned\nc59yzn3NOfe8c+6d67pe59xH+R34inPuz51z7XVd64PYSn78LjA4/B6AnwbwFgA/75x7yyre+zXY\nAsCvee/fAuAdAH6Fa/wYgOe8928C8Bz/Xhf7CIDnzd/rvNbfBfC33vs3A/gRhHWv3Xqdc08A+DCA\np733b0WAYX4Qa7jWBzbv/f/7PwDvBPB35u9nATy7ivd+gDV/BsBPAPg6gBu87waArz/stXEtTyJ8\nCd8D4HO8b13XugPgBbDGZO5fu/UCeALAdwDsI8DfPwfgJ9dxrQ/6b1VhvzZU9l3et5bmnHsjgLcB\n+AKA6957Sbq+DOD6Q1rWefsdAL+OKgv1uq71KQAHAP6IacofOOd6WMP1eu9vAfgEgBcB3AZw4r3/\nPNZwrQ9queB3zpxzfQB/DeBXvfcD+5gPl/2H3h5xzv0sgLve+3/5Xs9Zl7XS6gDeDuD3vfdvQ4B4\nV8LmdVkvc/n3I1ywbgLoOec+ZJ+zLmt9UFvVj/8WgNeZv5/kfWtlzrkGwg//z7z3n+bdd5xzN/j4\nDQB3H9b6jP0YgJ9zzn0LwF8AeI9z7k+xnmsFQqT3Xe/9F/j3pxAuBuu43vcBeMF7f+C9nwP4NIB3\nYT3X+kC2qh//FwG8yTn3lHOuiVBA+eyK3vtS5gJLxh8CeN57/9vmoc8CeIb/fwahFvAOo5l3AAAA\n00lEQVRQzXv/rPf+Se/9GxH28h+99x/CGq4VALz3LwP4jnPuB3nXewF8Feu53hcBvMM51+V34r0I\nxcl1XOuD2QoLKT8D4L8A/A+A33zYxY5XWd+PI4Ry/w7g3/jvZwBcQSisfQPAPwDYf9hrPbfudyMV\n/NZ2rQB+FMCXuL9/A2BvXdcL4LcAfA3AVwD8CYDWuq71Qf5lhF+2bBtqueCXLduGWv7xZ8u2oZZ/\n/NmybajlH3+2bBtq+cefLduGWv7xZ8u2oZZ//NmybajlH3+2bBtq/wuRWb7IP8Y95QAAAABJRU5E\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8c8427ca20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The probability that this is a doge is: 7.78294e-06\n"
     ]
    }
   ],
   "source": [
    "img = Image.open('grumpy_cat.jpeg')\n",
    "img = np.asarray(img.resize((img_height, img_width), Image.ANTIALIAS))/255\n",
    "plt.imshow(img)\n",
    "plt.show()\n",
    "p = combinedModel.predict(np.array([img]))\n",
    "print('The probability that this is a doge is: ' +str(p[0][0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lvSpoeMwZTBxbi2kyasHRokfGOZTfRiyWKUg/6fcq1VWz4cpQLTV+yIg0eTnQ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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8c790c0b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The probability that this is a doge is: 0.216492\n"
     ]
    }
   ],
   "source": [
    "img = Image.open('cat.jpeg')\n",
    "img = np.asarray(img.resize((img_height, img_width), Image.ANTIALIAS))/255\n",
    "plt.imshow(img)\n",
    "plt.show()\n",
    "p = combinedModel.predict(np.array([img]))\n",
    "print('The probability that this is a doge is: ' +str(p[0][0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Guess it doesn't do so well when there is random clutter:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kray+9C+uddX3TkhALiJ4pcI0bcjgXdnj8BshTHi8Itfr5QZN7ixTlhowBYC+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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8c7907bcc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The probability that this is a doge is: 0.327568\n"
     ]
    }
   ],
   "source": [
    "img = Image.open('grumpy2.jpeg')\n",
    "img = np.asarray(img.resize((img_height, img_width), Image.ANTIALIAS))/255\n",
    "plt.imshow(img)\n",
    "plt.show()\n",
    "p = combinedModel.predict(np.array([img]))\n",
    "print('The probability that this is a doge is: ' +str(p[0][0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  },
  "latex_envs": {
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 0
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
